R Notebook
Downsample data to save space
pupiltimecourse.data is dowsnampled but doesn’t have all participants < 400
but the script pupiltimecourseexp2.Rmd has what we need to do the same dowsnampling
Goal - take pupil timecourse, dowsnample and then mrge it it behavioural data so that we can do the additional arousal pupil analyses that check for timecourse things.
check gazeR or downsampling
to do, do the same arousal correlation with valence
library(ggplot2)
library(tidyverse)
install.packages("gazer")
library(gazer)
?gazer::downsample_gaze
?gazer::downsample_pupil
unique(X_tmp_df4_full$Condition)
# gazer::downsample_gaze%
tmp.df4_full_stim<- X_tmp_df4_full %>%
subset(screencontent == "stim")
# create bins
# install.packages("OneR")
install.packages("OneR")
library(OneR)
?bin
tmp.df4_full_stim$timebin<- OneR::bin(tmp.df4_full_stim$timerezero3, nbin = 60)
# dowsample by averaging?
colnames(tmp.df4_full_stim)
tmp.df4_full_stim_downs
tmp.df4_full_stim_downs <- tmp.df4_full_stim %>%
group_by(ssid, tNo, timebin, trial,trialUnq)%>%
summarise_at(c('timerezero3','pup_basCor', 'gaze_x_cor_pix', 'gaze_y_cor_pix'),
mean, na.rm = TRUE)
?saveRDS
saveRDS(tmp.df4_full_stim_downs, "tmp.df4_full_stim_downs.rds")
# create klarger bins
tmp.df4_full_stim_downs_jun2021
tmp.df4_full_stim_downs_jun2021_with_beh
range(tmp.df4_full_stim_downs$timerezero3)
tmp.df4_full_stim_downs_jun2021_with_beh$timebin2 <- OneR::bin(tmp.df4_full_stim_downs_jun2021_with_beh$timerezero3, nbin = 30)
tmp.df4_full_stim_downs_jun2021_with_beh2 <- tmp.df4_full_stim_downs_jun2021_with_beh %>%
group_by(ssid, tNo, timebin2, trial,trialUnq)%>%
summarise_at(c('timerezero3','pup_basCor', 'gaze_x_cor_pix', 'gaze_y_cor_pix'),
mean, na.rm = TRUE)
nrow(tmp.df4_full_stim_downs_jun2021_with_beh2)
87529pupil arousal
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, mediansplit_self_arousal, Alexithymia, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_self_arousal, pup_basCor, color = mediansplit_self_arousal,fill = mediansplit_self_arousal))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
# xlab("Arousal")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_self_valence)
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
brightness
Participants section
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(Group == "NT")%>%
# subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, mediansplit_ground_arousal, Alexithymia, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_arousal, pup_basCor, color = mediansplit_ground_arousal,fill = mediansplit_ground_arousal))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
# xlab("Arousal")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_ground_valence)
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, mediansplit_ground_arousal, Alexithymia, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_arousal, Mean, color = mediansplit_ground_arousal,fill = mediansplit_ground_arousal))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
# xlab("Arousal")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_ground_valence)+
# facet_grid(~BRIGHTNESS_mediansplit)+
ylim(90,130)
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
okay let’s create averages frio this entire sample and use those to devide the stimuli
self aropusal might be biased by problematic folks but averages for the entire sample should not
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid_num< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, self_arousal_lab, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(self_arousal_lab, pup_basCor, color = self_arousal_lab,fill = self_arousal_lab))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
# xlab("Arousal")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_self_valence)
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid_num< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, ground_arousal_lab, Alexithymia, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(ground_arousal_lab, pup_basCor, color = ground_arousal_lab,fill = ground_arousal_lab))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
xlab("Arousal")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
Resposne bias
ceiliing effect?
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, stimIAPS, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, pup_basCor))+
geom_point()+
# stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Arousal")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()+
ylim(-3.3,1.3)
ceiling effect?
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(mediansplit_ground_valence)%>%
mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
group_by(mediansplit_ground_valence)%>%
mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, pup_basCor))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary( geom = 'pointrange', alpha = .4, size = 1, colour = "Black") +
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
Ignoring unknown parameters: fun.y
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(mediansplit_ground_valence)%>%
mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
group_by(mediansplit_ground_valence)%>%
mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, pup_basCor))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary( geom = 'pointrange', alpha = .4, size = 1, colour = "Black") +
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_self_valence, pup_basCor))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
# db_full4new_stim_screen_pupil_nopract$arousal_outler
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_sample_arousal2, arousal))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_sample_valence2)+
ggpubr::stat_cor()
Ignoring unknown parameters: fun.y
otehr confounds - eye movements
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, mean_fix_dur))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
unique(db_full4new_stim_screen_pupil_nopract$ValenceMeanThisSample)
median(db_full4new_stim_screen_pupil_nopract$ValenceMeanThisSample)
median(db_full4new_stim_screen_pupil_nopract$valence)
median(db_full4new_stim_screen_pupil_nopract$arousal)
median(db_full4new_stim_screen_pupil_nopract$ArousalMeanThisSample)
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_self_valence, mean_fix_dur))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
# data loss gaze
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, fix_count))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, SacAmp))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, sdevgaze))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, gaze_loss_prop))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
ylim(-3.3,1.3)
db_full4new_stim_screen_pupil_nopract%>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
group_by(ssid, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(mediansplit_ground_valence)%>%
# mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
ggplot(aes(mediansplit_ground_valence, total_fix_dur))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
# geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
geom_smooth(method = 'lm', se = F)+
p$graphstyle+
xlab("Valence")+
# ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
let’s model it formally
remember baseline corection is equivalendt to stimuli intercept
pupil_arousal_findings<- list()
library(lmerTest)
unique(db_full4new_stim_screen_pupil_nopract$stimIAPS)
unique(substr(db_full4new_stim_screen_pupil_nopract$stimIAPS, 1,8))
db_full4new_stim_screen_pupil_nopract$Label<- substr(db_full4new_stim_screen_pupil_nopract$stimIAPS, 1,8)
db_full4new_stim_screen_pupil_nopract<- left_join(db_full4new_stim_screen_pupil_nopract, imageJ_IAPS, by = "Label")
nrow(db_full4new_stim_screen_pupil_nopract)
# rows = 2858
db_full4new_stim_screen_pupil_nopract$Mean_gray_z<- scale(db_full4new_stim_screen_pupil_nopract$Mean, center = TRUE, scale = TRUE)[,1]
options(contrasts = c("contr.sum","contr.poly"))
options(scipen = 999)
db_full4new_stim_screen_pupil_nopract$ssid_num <- as.numeric(as.character(db_full4new_stim_screen_pupil_nopract$ssid))
pupil_arousal_findings$pupil_from_arousal <- lmer(pup_basCor ~ arousal_c*Mean_gray_z +(1 +Mean_gray_z | ssid) + (0+arousal_c| ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 &
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_arousal)
# singularity (drop arousal c slope)
# is there a coorelationbtween mean arousal rating and meagray(
db_full4new_stim_screen_pupil_nopract%>%
group_by(stimIAPS,ground_valence_lab) %>%
summarise_at(c('arousal_c', 'Mean_gray_z'), mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, Mean_gray_z, color = ground_valence_lab))+
geom_point()+
ggpubr::stat_cor()
# <- predict(pupil_arousal_findings$pupil_from_arousal)
db_full4new_stim_screen_pupil_nopract %>%
group_by(stimIAPS) %>%
# summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# summarise_at(c('arousal_c', 'Mean_gray_z'), mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, pup))+
geom_smooth(aes(group = ssid, alpha = ssid), method = 'lm', se = F)
# geom_smooth(aes(group = stimIAPS), method = 'lm', se = F)
ggpubr::stat_cor()
cor.test(db_full4new_stim_screen_pupil_nopract\(, db_full4new_stim_screen_pupil_nopract\))
# if we put brighteness and arousal we see no interaction which means we probably don't need to worry about brighteness, it doesn't change effects of arousal self report
# db_full4new_stim_pupil$pup
pupil_arousal_findings$pupil_from_arousal <- lmer(pup_basCor ~ arousal_c+Mean_gray_z +(1 +Mean_gray_z | ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract$pup, pupil_outlier == FALSE & ssid_num< 500&
arousal_outler == FALSE))
install.packages('usdm')
usdm::vif(as.data.frame(db_full4new_stim_screen_pupil_nopract[,c('arousal', 'valence')]))
pupil_arousal_findings$pupil_from_arousal_withval <- lmer(pup_basCor ~ arousal_c*valence_c* Mean_gray_z +(1 +Mean_gray_z | ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500&
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_arousal_withval)
car::vif(pupil_arousal_findings$pupil_from_arousal_withval)
lmerTest::step(pupil_arousal_findings$pupil_from_arousal_withval)
# control for baseline arousal ratings
pupil_arousal_findings$pupil_from_arousal_withval
summary(lmer(pup_basCor ~ arousal_c*valence_c+ Mean_gray_z +(1 | ssid) + (1|stimIAPS),
# (1|ArousalMean)+ (1|ArousalMean), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & Group == "NT" &
arousal_outler == FALSE)))
# arousal_c:valence_c 0 35.52 35.52 1 2150.19 13.5760 0.0002348 ***
pupil_arousal_findings$arousal_from_pupil <- lmer(arousal ~ pup_basCor * Mean_gray_z +(1+pup_basCor | ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610&
arousal_outler == FALSE))
summary(pupil_arousal_findings$arousal_from_pupil)
# what about valence
pupil_arousal_findings$pupil_from_valence <- lmer(pup_basCor ~ valence_c+Mean_gray_z +(1 +Mean_gray_z | ssid) +
(0 +valence_c | ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610&
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_arousal)
summary(pupil_arousal_findings$pupil_from_valence)
Valence and arousal
pupil_arousal_findings$pupil_from_ar_vale <- lmer(pup_basCor ~ (valence_c* arousal_c) + Mean_gray_z +
(1 + Mean_gray_z | ssid),
# (0+ valence_c* arousal_c | ssid),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 &
arousal_outler == FALSE))
db_full4new_stim_screen_pupil_nopract$total_fix_dur_z<- scale(db_full4new_stim_screen_pupil_nopract$total_fix_dur, center = TRUE, scale = TRUE)[,1]
pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * total_fix_dur_z)+ Mean_gray_z +
+
(1 + Mean_gray_z | ssid),
# (0+ total_fix_dur_z | ssid),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 &
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_ar_val_gaze )
summary(pupil_arousal_findings$pupil_from_ar_vale)
car::vif(pupil_arousal_findings$pupil_from_ar_val_gaze)
anova(pupil_arousal_findings$pupil_from_ar_vale, pupil_arousal_findings$pupil_from_arousal)
# interaction valence and arousal
control for gaze
# avg fix dur
db_full4new_stim_screen_pupil_nopract$mean_fix_dur_z<- scale(db_full4new_stim_screen_pupil_nopract$mean_fix_dur, center = TRUE, scale = TRUE)[,1]
pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * mean_fix_dur_z)+ Mean_gray_z +
+
(1 + Mean_gray_z | ssid),
# (0+ total_fix_dur_z | ssid),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 &
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_ar_val_gaze )
interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_val_gaze , pred = arousal_c, modx = mean_fix_dur_z)
# fix count
db_full4new_stim_screen_pupil_nopract$fix_count_z<- scale(db_full4new_stim_screen_pupil_nopract$fix_count, center = TRUE, scale = TRUE)[,1]
pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * fix_count_z)+ Mean_gray_z +
+
(1 + Mean_gray_z | ssid),
# (0+ total_fix_dur_z | ssid),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 &
arousal_outler == FALSE))
summary(pupil_arousal_findings$pupil_from_ar_val_gaze )
interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_val_gaze , pred = arousal_c, modx = mean_fix_dur_z)interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_vale , pred = valence_c, modx = arousal_c)
interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_vale , pred =arousal_c , modx = valence_c)
car::vif(pupil_arousal_findings$pupil_from_ar_vale) #curoff 2.5?
cor.test(db_full4new_stim_screen_pupil_nopract$valence_c, db_full4new_stim_screen_pupil_nopract$arousal_c)
rmcorr_valence_arousal <- rmcorr::rmcorr(participant = "ssid", measure1 = "valence_c", measure2 = "arousal_c", data = db_full4new_stim_screen_pupil_nopract)
plot(rmcorr_valence_arousal)
?rmcorr
db_full4new_stim_screen_pupil_nopract %>%
group_by(stimIAPS)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(ArousalMean, ValenceMean))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
theme_classic()
db_full4new_stim_screen_pupil_nopract %>%
group_by(stimIAPS)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, valence_c))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
theme_classic()
# with arousal as DV
pupil_arousal_findings$aroual_from_pup <- lmer(arousal ~ valence_c*pup_basCor * BRIGHTNESSc + (1| stimIAPS)+
(1 | ssid)+(0 +valence_c | ssid), REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610&
arousal_outler == FALSE))
summary(pupil_arousal_findings$aroual_from_pup)
anova(pupil_arousal_findings$aroual_from_pup)
interactions::interact_plot(pupil_arousal_findings$aroual_from_pup , pred = valence_c, modx = pup_basCor)
interactions::interact_plot(pupil_arousal_findings$aroual_from_pup, pred =pup_basCor , modx = valence_c)
# for positive valenced stimuli participants reporte higher arousal to more dilated pupils
#for negatively valence they report feeling negative even when they don't show strog pupil dilation
# same pattern
# pupil is more alighned with objective arousal in the positive trials
mixed model is consistent wth the previous plot, arousal effects on pupil is modulated by valence. in other words there is a consistent effect between pupil and self-repot of arousal more for positive emotions.
analyse the correlationla way since we are aggregating we don’t need to worry too much about brighteness as every person sees every stimuli in all brightness levels CONCORDANCE
db_full4new_stim_screen_pupil_nopract%>%
group_by(mediansplit_self_valence_thissample, stimIAPS)%>%
summarise_at(c("arousal", 'valence'), mean, na.rm = TRUE)%>%
arrange(stimIAPS)
test_anovas<- db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)
db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
theme_classic()+
geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)+
# ggpubr::stat_compare_means(paired = TRUE)+
ggpubr::stat_compare_means()+
facet_grid(~mediansplit_sample_arousal2)
# let's identify the folks that show less of a difference between conditions.
flag_pos_neg<- db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_valence2)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
mutate(cor_pos_neg_diff = cortest[mediansplit_sample_valence2 == "More positive"] -
cortest[mediansplit_sample_valence2 == "More negative"])%>%
mutate(cor_pos_neg_diff_lab = if_else(cor_pos_neg_diff>0, "pos > neg", "neg > pos"))%>%
group_by(ssid,cor_pos_neg_diff_lab)%>%
summarise_at(c("cor_pos_neg_diff"), mean, na.rm = TRUE)
# subset(cor_pos_neg_diff<= 0)%>%
ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
theme_classic()+
geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)
# ggpubr::stat_compare_means(paired = TRUE)+
ggpubr::stat_compare_means()
testcor <-cor(db_full4new_stim_screen_pupil_nopract$arousal, db_full4new_stim_screen_pupil_nopract$valence)
testcor
db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_valence2, mediansplit_sample_arousal2)%>%
# mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
mutate(cortest = cor(arousal_c, pup_basCor, method = 'kendall'))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
theme_classic()+
facet_grid(~mediansplit_sample_arousal2)+
geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)
# ggpubr::stat_compare_means(paired = TRUE)+
# ggpubr::stat_compare_means()
db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
# mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
mutate(cortest = cor(valence_c, pup_basCor, method = 'kendall'))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_sample_arousal2, cortest, color = mediansplit_sample_arousal2))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
theme_classic()+
facet_grid(~mediansplit_sample_valence2)+
geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)
same as plot above but with valence in the correlation
db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_sample_arousal2)%>%
mutate(cortest = cor.test(valence_c, pup_basCor)$estimate)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_sample_arousal2, cortest, color = mediansplit_sample_arousal2))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
theme_classic()
# ggpubr::stat_compare_means(paired = TRUE)+
ggpubr::stat_compare_means()
# ground
db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_ground_valence)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_valence, cortest, color = mediansplit_ground_valence))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .3)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
# ggpubr::stat_compare_means(paired = TRUE)+
# ggpubr::stat_compare_means()
theme_classic() db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_ground_valence, mediansplit_ground_arousal)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_valence, cortest, color = mediansplit_ground_valence))+
geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
geom_jitter(width = .1, alpha = .3)+
stat_summary( geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
ylab("R pupil vs self report arousal")+
# ggpubr::stat_compare_means(paired = TRUE)+
# ggpubr::stat_compare_means()
theme_classic()+
facet_grid(~mediansplit_ground_arousal)
Ignoring unknown parameters: fun.y
db_pupil_concordance_agg_ground <- db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_ground_valence)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)
afex::aov_ez(id = "ssid",
data = db_pupil_concordance_agg,
between = NULL,
within = 'mediansplit_ground_valence',
dv = 'cortest')
unique(db_full4new_stim_screen_pupil_nopract$tNo)
db_pupil_concordance_agg_self <- db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE)%>%
subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
subset(!is.na(Alexithymia))%>%
# subset(Group == "NT")%>%
subset(ssid_num<500)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_self_arousal, mediansplit_self_valence)%>%
mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)
afex::aov_ez(id = "ssid",
data = test_anovas,
between = NULL,
within = 'mediansplit_sample_valence2',
dv = 'cortest')
afex::aov_ez(id = "ssid",
data = subset(test_anovas, mediansplit_sample_valence2 == "More positive"),
between = NULL,
within = 'mediansplit_sample_arousal2',
dv = 'cortest')
afex::aov_ez(id = "ssid",
data = db_pupil_concordance_agg_self,
between = NULL,
within = 'mediansplit_ground_valence',
dv = 'cortest')
# valence and arousal
afex::aov_ez(id = "ssid",
data = test_anovas,
between = NULL,
within = c('mediansplit_sample_arousal2','mediansplit_sample_valence2'),
dv = 'cortest')
db_parsed_f_summ<- db_parsed_f %>%
group_by(tNo, ssid)%>%
summarise_at(c('Duration', 'mean_x', 'mean_y'), sum, na.rm = TRUE)
ggplot(aes(tNo, Duration))+
stat_summary(geom = 'pointrange')+
geom_point(alpha = .1)
unique(db_parsed_f_summ$ssid)
unique(db_parsed_f_summ$tNo)
unique(db_full4new_stim_screen_pupil_nopract$tNo)
unique(db_full4new_stim_screen_pupil_nopract$ssid)
nrow(db_full4new_stim_screen_pupil_nopract)
nrow(db_parsed_f_summ)
table(is.na(db_parsed_f_summ$ssid))
db_parsed_f_summ$total_fix_dur<- db_parsed_f_summ$Duration
db_parsed_f_summ$tNo<- as.character(db_parsed_f_summ$tNo)
db_parsed_f_summ$ssid<- as.character(db_parsed_f_summ$ssid)
db_full4new_stim_screen_pupil_nopract$Duration<- NULL
db_full4new_stim_screen_pupil_nopract$total_fix_dur<- NULL
db_full4new_stim_screen_pupil_nopract<- left_join(db_full4new_stim_screen_pupil_nopract, db_parsed_f_summ)
I HAVE THE IMPRESSION THAT WHAT THIS ANALYSES GIVES US IS THE SAME AS THE MIXED MODEL AS THIS CONCIORDANCE CAN BE EXPRESSED IN TERMS FO THE SLOPE OF THE AOUSAL AND PUPIL RELATION
SO ION OTHER WORDS IS not that pupil tracksvalence is that arousal based pupil is modulated by valence
by why is that - bias is responding? eg.. people response they feel negative even when they don’t (ie. when we have no objective indicator that they do feel) - is it something about the stimuli, positive arousal is sexual here? - or arousal interferes with appraisal. high arousing stimuli are distracting??
timecourse models of pupil
Pupil tracks valence
db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler== FALSE)%>%
subset(ssid< 500)%>%
subset(pupil_outlier == FALSE)%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, self_arousal_lab, Alexithymia, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(self_arousal_lab, pup_basCor, color = self_arousal_lab,fill = self_arousal_lab))+
geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
# geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
geom_jitter(width = .1, alpha=.2)+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
stat_summary(geom = 'pointrange', width =1)+
p$graphstyle+
xlab("Arousal")+
ylab("Baseline corrected pupil")+
scale_color_brewer(palette = "Dark2")+
scale_fill_brewer(palette = "Dark2")+
facet_grid(~mediansplit_self_valence)
Ignoring unknown parameters: fun.yIgnoring unknown parameters: width
combine timecourse
let’s put pupil scr and ht prediction on subjective arousal
tmp.df4_full_stim_downs_jun2021_with_beh2$timebin2
[1] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[5] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[9] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[13] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[17] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[21] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[25] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[29] (6.01,6.23] (0.0411,0.261] (0.261,0.474] (0.474,0.687]
[33] (0.687,0.9] (0.9,1.11] (1.11,1.33] (1.33,1.54]
[37] (1.54,1.75] (1.75,1.96] (1.96,2.18] (2.18,2.39]
[41] (2.39,2.6] (2.6,2.82] (2.82,3.03] (3.03,3.24]
[45] (3.24,3.46] (3.46,3.67] (3.67,3.88] (3.88,4.1]
[49] (4.1,4.31] (4.31,4.52] (4.52,4.73] (4.73,4.95]
[53] (4.95,5.16] (5.16,5.37] (5.37,5.59] (5.59,5.8]
[57] (5.8,6.01] (6.01,6.23] (0.0411,0.261] (0.261,0.474]
[61] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[65] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[69] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[73] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[77] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[81] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[85] (5.59,5.8] (5.8,6.01] (6.01,6.23] (0.0411,0.261]
[89] (0.261,0.474] (0.474,0.687] (0.687,0.9] (0.9,1.11]
[93] (1.11,1.33] (1.33,1.54] (1.54,1.75] (1.75,1.96]
[97] (1.96,2.18] (2.18,2.39] (2.39,2.6] (2.6,2.82]
[101] (2.82,3.03] (3.03,3.24] (3.24,3.46] (3.46,3.67]
[105] (3.67,3.88] (3.88,4.1] (4.1,4.31] (4.31,4.52]
[109] (4.52,4.73] (4.73,4.95] (4.95,5.16] (5.16,5.37]
[113] (5.37,5.59] (5.59,5.8] (5.8,6.01] (6.01,6.23]
[117] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[121] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[125] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[129] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[133] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[137] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[141] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[145] (6.01,6.23] (0.0411,0.261] (0.261,0.474] (0.474,0.687]
[149] (0.687,0.9] (0.9,1.11] (1.11,1.33] (1.33,1.54]
[153] (1.54,1.75] (1.75,1.96] (1.96,2.18] (2.18,2.39]
[157] (2.39,2.6] (2.6,2.82] (2.82,3.03] (3.03,3.24]
[161] (3.24,3.46] (3.46,3.67] (3.67,3.88] (3.88,4.1]
[165] (4.1,4.31] (4.31,4.52] (4.52,4.73] (4.73,4.95]
[169] (4.95,5.16] (5.16,5.37] (5.37,5.59] (5.59,5.8]
[173] (5.8,6.01] (6.01,6.23] (0.0411,0.261] (0.261,0.474]
[177] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[181] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[185] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[189] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[193] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[197] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[201] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[205] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[209] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[213] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[217] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[221] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[225] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[229] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[233] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[237] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[241] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[245] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[249] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[253] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[257] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[261] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[265] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[269] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[273] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[277] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[281] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[285] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[289] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[293] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[297] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[301] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[305] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[309] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[313] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[317] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[321] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[325] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[329] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[333] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[337] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[341] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[345] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[349] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[353] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[357] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[361] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[365] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[369] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[373] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[377] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[381] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[385] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[389] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[393] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[397] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[401] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[405] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[409] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[413] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[417] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[421] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[425] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[429] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[433] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[437] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[441] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[445] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[449] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[453] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[457] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[461] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[465] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[469] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[473] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[477] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[481] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[485] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[489] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[493] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[497] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[501] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[505] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[509] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[513] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[517] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[521] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[525] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[529] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[533] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[537] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[541] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[545] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[549] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[553] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[557] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[561] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[565] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[569] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[573] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[577] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[581] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[585] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[589] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[593] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[597] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[601] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[605] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[609] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[613] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[617] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[621] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[625] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[629] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[633] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[637] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[641] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[645] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[649] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[653] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[657] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[661] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[665] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[669] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[673] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[677] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[681] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[685] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[689] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[693] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[697] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[701] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[705] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[709] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[713] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[717] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[721] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[725] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[729] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[733] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[737] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[741] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[745] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[749] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[753] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[757] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[761] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[765] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[769] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[773] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[777] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[781] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[785] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[789] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[793] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[797] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[801] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[805] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[809] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[813] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[817] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[821] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[825] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[829] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[833] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[837] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[841] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[845] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[849] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[853] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[857] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[861] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[865] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[869] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[873] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[877] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[881] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[885] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[889] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[893] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[897] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[901] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[905] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[909] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[913] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[917] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[921] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[925] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[929] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[933] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[937] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[941] (3.46,3.67] (3.67,3.88] (3.88,4.1] (4.1,4.31]
[945] (4.31,4.52] (4.52,4.73] (4.73,4.95] (4.95,5.16]
[949] (5.16,5.37] (5.37,5.59] (5.59,5.8] (5.8,6.01]
[953] (6.01,6.23] (6.23,6.44] (0.0411,0.261] (0.261,0.474]
[957] (0.474,0.687] (0.687,0.9] (0.9,1.11] (1.11,1.33]
[961] (1.33,1.54] (1.54,1.75] (1.75,1.96] (1.96,2.18]
[965] (2.18,2.39] (2.39,2.6] (2.6,2.82] (2.82,3.03]
[969] (3.03,3.24] (3.24,3.46] (3.46,3.67] (3.67,3.88]
[973] (3.88,4.1] (4.1,4.31] (4.31,4.52] (4.52,4.73]
[977] (4.73,4.95] (4.95,5.16] (5.16,5.37] (5.37,5.59]
[981] (5.59,5.8] (5.8,6.01] (6.01,6.23] (6.23,6.44]
[985] (0.0411,0.261] (0.261,0.474] (0.474,0.687] (0.687,0.9]
[989] (0.9,1.11] (1.11,1.33] (1.33,1.54] (1.54,1.75]
[993] (1.75,1.96] (1.96,2.18] (2.18,2.39] (2.39,2.6]
[997] (2.6,2.82] (2.82,3.03] (3.03,3.24] (3.24,3.46]
[ reached getOption("max.print") -- omitted 86529 entries ]
30 Levels: (0.0411,0.261] (0.261,0.474] (0.474,0.687] ... (6.23,6.44]# check
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(ground_arousal_lab))%>%
ggplot(aes(x = timerezero3, y = pup_basCor))+
stat_smooth(aes(group= ground_arousal_lab, color = ground_arousal_lab), fun = mean,geom = "line",
se = F, alpha = .1, size = 1.5)+
stat_smooth(aes(group=ground_arousal_lab, color = ground_arousal_lab), size = 3)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
xlim(0,6)+
facet_grid(~mediansplit_self_valence)
Ignoring unknown parameters: fun
pupil timecourse
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(mediansplit_sample_arousal2))%>%
subset(!is.na(mediansplit_sample_arousal2))%>%
ggplot(aes(x = timerezero3, y = pup_basCor))+
stat_smooth(aes(group= mediansplit_sample_valence2, color = mediansplit_sample_valence2), fun = mean,geom = "line",
se = F, alpha = .1, size = 1.5)+
stat_smooth(aes(group=mediansplit_sample_valence2, color = mediansplit_sample_valence2), size = 3)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
xlim(0,6)+
facet_grid(~mediansplit_sample_arousal2)
Ignoring unknown parameters: fun
unique(tmp.df4_full_stim_downs_jun2021_with_beh$condition)
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(mediansplit_sample_arousal2))%>%
subset(valencearousal_outliers == TRUE) %>%
# subset(!is.na(mediansplit_sample_arousal2))%>%
ggplot(aes(x = timerezero3, y = pup_basCor))+
stat_smooth(aes(group= mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), fun = mean,geom = "line",
se = F, alpha = .1, size = 1.5)+
stat_smooth(aes(group=mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), size = 3)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
xlim(0,6)+
facet_grid(mediansplit_sample_valence2~.)+
geom_hline(yintercept = 0)
Ignoring unknown parameters: fun
unique(tmp.df4_full_stim_downs_jun2021_with_beh$timebin2) # 30 time bins
timecourse analyss using lmer
1- select a bin 2 - fit a lmer 3 - save statistics (estimate and p value) 3 - create clusters (p value < .05 in at least 3 consecutive bins)
tmp.df4_full_stim_downs_jun2021_with_behnew$
i = 1
nbins<- unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)
tmp.df4_full_stim_downs_jun2021_with_behnew
unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)
lmer_bin_pup<- list()
# <- nrow(nbins)
nbins<- unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)
tmp.df4_full_stim_downs_jun2021_with_behnew$Label<- substr(tmp.df4_full_stim_downs_jun2021_with_behnew$stimIAPS, 1,8)
imageJ_IAPS$Label
nrow(tmp.df4_full_stim_downs_jun2021_with_behnew)
# 62921
nrow(left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, imageJ_IAPS))
tmp.df4_full_stim_downs_jun2021_with_behnew<- left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, imageJ_IAPS)
tmp.df4_full_stim_downs_jun2021_with_behnew$Mean_gray_z<- scale(tmp.df4_full_stim_downs_jun2021_with_behnew$Mean, scale = TRUE, center = TRUE)[,1]
test<- lmer(pup_basCor ~ arousal_c+Mean_gray_z +(1 +Mean_gray_z | ssid),
REML = FALSE,
data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
timebin2 == nbins[b]))
summary(test)
tmp.df4_full_stim_downs_jun2021_with_behnew$tNo
"(0.0411,0.261]"
subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
timebin2 == nbins[28] & ssid == 341 & tNo == 3)
test
rm(i)
i = 1 # remember to always rezero it
# b = 34
nbins<- nbins[1:28]
unique(nbins)
# nbins<- as
# positive
# create number s for time bins
tmp.df4_full_stim_downs_jun2021_with_behnew<- tmp.df4_full_stim_downs_jun2021_with_behnew%>%
group_by(ssid, stimIAPS)%>%
mutate(timebin_no = 1:n())
tmp.df4_full_stim_downs_jun2021_with_behnew%>%
# group_by(ssid, stimIAPS)
ggplot(aes(timebin_no))+
geom_histogram()+
geom_vline(xintercept = 28)+
geom_hline(yintercept = 200)
negative lmer
timecourse_result_df$Valence <- "More negative"
timecourse_result_df_neg$Valence<- "More positive"
bind_rows(timecourse_result_df,timecourse_result_df_neg)%>%
ggplot(aes(timebins, t, color = Valence))+
geom_line(size = 2)+
geom_line(aes(y = p), linetype = "dashed", size = 2)+
geom_hline(yintercept = .05, color = 'red', size = 1.5)
use valence to pupil
use valence to pupil
valence for high vs low
velocity analyses
Velocity by valence
Negative velocity
A few plots for velocity
tmp.df4_full_stim_downs_jun2021_with_behnew%>%
group_by(mediansplit_self_valence, timebin2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(timerezero3, cor_arous_pup, color = mediansplit_self_valence))+
# geom_s
stat_summary(fun = mean, geom = 'pointrange')+
stat_summary(aes(y = cor_arous_pup_pval), fun = mean, geom = 'line', linetype = "dashed")+
geom_hline(yintercept = .1, linetype = "dashed")
geom_rect(aes(xmin = timerezero3, xmax = dplyr::lead(timerezero3), ymin =.63 , ymax =.66 , fill = pval_sign),
alpha = 0.8)
# stat_smooth(aes(group = ssid), se = F, alpha = .1)
# mutate(
# model = map(data, ~lm(pup_basCor ~ arousal_c, data = .)),
# cor = map(data, possibly(
# ~tidy(cor.test(.x$pup_basCor, .x$arousal_c), 3), otherwise = data.frame())
# )
# )
self arousal
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(ground_arousal_lab))%>%
ggplot(aes(x = timerezero3, y = pup_basCor))+
stat_smooth(aes(group= ground_arousal_lab, color = ground_arousal_lab), fun = mean,geom = "line",
se = F, alpha = .1, size = 1.5)+
stat_smooth(aes(group=ground_arousal_lab, color = ground_arousal_lab), size = 3)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
xlim(0,6)+
facet_grid(~mediansplit_self_valence)
# plot with valence
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(ground_arousal_lab))%>%
group_by(ssid,Alexithymia, mediansplit_ground_valence, timebin2)%>%
ggplot(aes(x = timerezero3, y = pup_basCor))+
stat_smooth(aes(group= mediansplit_ground_valence, color = mediansplit_ground_valence), fun = mean,geom = "line",
se = F, size = 1.5)+
# stat_smooth(aes(group=ground_valence_lab, color = ground_valence_lab), size = 3)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
xlim(0,6)
test<- cor.test(db_2021_pupil_summ$hr_interp,db_2021_pupil_summ$pup_basCor)
test$p.value
tmp.df4_full_stim_downs_jun2021_with_beh$ssid_ground_valence<- paste0(tmp.df4_full_stim_downs_jun2021_with_beh$ssid, paste0(tmp.df4_full_stim_downs_jun2021_with_beh$ground_arousal_lab))
tmp.df4_full_stim_downs_jun2021_with_beh %>%
subset(!is.na(self_arousal_lab))%>%
db_full4new_stim_screen_pupil_nopract
group_by(ssid,Alexithymia, ground_valence_lab, timebin2)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
mutate(pcortest = cor.test(arousal_c, pup_basCor)$p.value)%>%
group_by(ssid,Alexithymia, self_arousal_lab, timebin2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE) %>%
ggplot(aes(x = timerezero3, y = cortest))+
stat_smooth(aes(group= self_arousal_lab, color = self_arousal_lab), fun = mean,geom = "line",
se = F, size = 1.5)+
theme_classic()+
ylab("Pupil size (z)")+
xlab("Time (s)")+
# p$graphstyle+
scale_color_brewer(palette = "Dark2")+
geom_hline(yintercept = .05, linetype = "dashed")+
xlim(0,6)
# does arousing stimuli impairs appraisal
tmp.df4_full_stim_downs_jun2021_with_beh %>%
ggplot(aes(ground_arousal_lab, BRIGHTNESS))+
geom_boxplot()+
geom_point()+
# stat_summary(geom = 'boxplot')+
facet_grid(~mediansplit_self_valence) db_full4new_stim_screen_pupil_nopract %>%
subset(pupil_outlier == FALSE & arousal_outler == FALSE & !is.na(Alexithymia) & ssid!= 610)%>%
# group_by(ssid,Alexithymia, is_valence_high)%>%
group_by(ssid,Alexithymia, mediansplit_self_valence)%>%
mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
group_by(ssid,Alexithymia, mediansplit_self_valence, self_arousal_lab)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_self_valence, cortest, color = mediansplit_self_valence))+
geom_bar(stat="summary", fun.y = "mean")+
geom_jitter(width = .1, alpha = .1)+
stat_summary( geom = 'pointrange')+
# stat_summary(aes(group = ssid), geom = 'line')+
ylab("R pupil vs self report arousal")+
# ggpubr::stat_compare_means(paired = TRUE)+
# ggpubr::stat_compare_means(comparisons = my_comparisons, paired = TRUE)
facet_grid(~self_arousal_lab)
SCR
unique(db_full4new_stim_screen_pupil_nopract$ssid)
View(flag_pos_neg)
db_full4new_stim_screen_pupil_nopract$mediansplit_self_arousal <- if_else(db_full4new_stim_screen_pupil_nopract$arousal> median(db_full4new_stim_screen_pupil_nopract$arousal, na.rm = TRUE), "High", "Low")
db_full4new_stim_screen_pupil_nopract$mediansplit_self_arousal_thissample <- if_else(db_full4new_stim_screen_pupil_nopract$arousal> median(db_full4new_stim_screen_pupil_nopract$ArousalMeanThisSample, na.rm = TRUE), "High", "Low")
unique(db_full4new_stim_screen_pupil_nopract2$ssid)
db_full4new_stim_screen_pupil_nopract_nt<- subset(db_full4new_stim_screen_pupil_nopract, ssid_num<500)
nrow(db_full4new_stim_screen_pupil_nopract_nt)
# 2233
# nrow(left_join(db_full4new_stim_screen_pupil_nopract_nt, flag_pos_neg))
db_full4new_stim_screen_pupil_nopract_nt<- left_join(db_full4new_stim_screen_pupil_nopract_nt, flag_pos_neg)
unique(db_full4new_stim_screen_pupil_nopract_nt$stimDescription)
db_full4new_stim_screen_pupil_nopract_nt %>%
subset(ssid_num< 500)%>%
subset(pp_arousal_outler == FALSE)%>%
subset( arousal_outler == FALSE )%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid)%>%
mutate(BIO_CDA.SCR_z = scale((BIO_CDA.SCR))[,1])%>%
ungroup()%>%
group_by(ssid,cor_pos_neg_diff_lab, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_self_valence, BIO_CDA.SCR_z))+
stat_summary(geom = 'pointrange')+
# geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
geom_jitter(width = .2, alpha = .2)+
stat_summary(aes(group = ssid), geom = 'line', color = "gray", alpha = .1)+
# geom_boxplot(alpha = .1)+
# facet_grid(~cor_pos_neg_diff_lab)+
ggpubr::stat_compare_means()
BIO_CDA.ISCR_z
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid)%>%
mutate(BIO_TTP.AmpSum_z = scale(BIO_TTP.AmpSum, center = TRUE, scale = TRUE)[,1])%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, BIO_TTP.AmpSum_z))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid,mediansplit_sample_valence2)%>%
# mutate(BIO_TTP.AmpSum_z = scale(BIO_TTP.AmpSum, center = TRUE, scale = TRUE)[,1])%>%
# summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal, BIO_CDA.ISCR_z))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
facet_grid(~mediansplit_sample_valence2)
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid,mediansplit_sample_valence2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, BIO_CDA.PhasicMax_z, color =mediansplit_sample_valence2))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid,ground_arousal_lab)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal_c, BIO_CDA.PhasicMax_z, color =ground_arousal_lab))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
scr model
unique(db_full4new_stim_screen_pupil_nopract_nt$condition)
pupil_arousal_findings$arousal_scr <- lmer(log(BIO_CDA.SCR+.1) ~ arousal_c*TASc +(1|ssid) + (1|stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt,
pp_arousal_outler == FALSE &
mediansplit_self_valence == "More positive"))
summary(pupil_arousal_findings$arousal_scr)
anova(pupil_arousal_findings$arousal_phas_max)
BIO_CDA.PhasicMax
pupil_arousal_findings$arousal_scr <- lmer(BIO_CDA.PhasicMax_z ~ valence_c +(1+arousal_c|ssid),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt,
pp_arousal_outler == FALSE))
# mediansplit_self_valence == "More positive"))
summary(pupil_arousal_findings$arousal_scr)
anova(pupil_arousal_findings$arousal_phas_max)
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid, cor_pos_neg_diff_lab)%>%
ggplot(aes(cor_pos_neg_diff_lab, TAS))+
geom_point()+
stat_summary(geom = 'pointrange')summary(pupil_arousal_findings\(arousal_scr) anova(pupil_arousal_findings\)arousal_phas_max)
test<- subset(db_full4new_stim_screen_pupil_nopract, pp_arousal_outler == FALSE & ssid == 310) test\(Alexithymia table(test\)mediansplit_self_valence)
try the anova grouping
afex::aov_ez(data = (db_full4new_stim_screen_pupil_nopract %>%
subset(pp_arousal_outler == FALSE)%>%
# subset(is_scr_nonresponder == FALSE )%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid, mediansplit_ground_valence)%>%
# group_by(ssid, Alexithymia,mediansplit_self_arousal, mediansplit_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)),
id = "ssid",
between = NULL,
within = c("mediansplit_ground_valence"),
dv = "BIO_CDA.SCR_z")
colnames(db_full4new_stim_screen_pupil_nopract)
ggcorrplot::ggcorrplot(cor(db_full4new_stim_screen_pupil_nopract[,c(19,115,125:128,27,26)], use = "complete"))
BIO_CDA.SCR_z
indovidual plots
facet_hist_scr<- db_full4new_stim_screen_pupil_nopract %>%
subset(ssid > 303)%>%
subset(arousal_outler == FALSE )%>%
subset(!is.na(Alexithymia))%>%
ggplot(aes(log1p(BIO_CDA.SCR+1)))+
geom_histogram(bins = )+
facet_wrap(~ssid)
what’s up with non responders
312
318 only one rspinse?
320
321
327
329
332
335 (only one response)
342
343
600
603
606 (only one response)
608
900
56- 15
that would leave us with 41 people
unique(db_full4new_stim_screen_pupil_nopract$ssid)
range(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)
mean(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)
sd(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)
db_full4new_stim_screen_pupil_nopract%>%
ggplot(aes(BIO_CDA.SCR))+
geom_histogram(bins = 500)
xlim(0,.01)
db_full4new_stim_screen_pupil_nopract%>%
subset(ssid == 312)%>%
ggplot(aes(BIO_CDA.AmpSum))+
geom_histogram(bins = 500)+
xlim(0,.05)
db_full4new_stim_screen_pupil_nopract$is_scr_nonresponder<- if_else(db_full4new_stim_screen_pupil_nopract$BIO_CDA.AmpSum< .1, TRUE, FALSE)
db_full4new_stim_screen_pupil_nopract %>%
subset(ssid > 303)%>%
subset(arousal_outler == FALSE )%>%
subset(!is.na(Alexithymia))%>%
ggplot(aes(log1p(BIO_CDA.SCR+1)))+
geom_histogram(bins = )+
facet_grid(~is_scr_nonresponder)
facet_wrap(~ssid)
db_full4new_stim_screen_pupil_nopract%>%
group_by(ssid,is_scr_nonresponder)%>%
mutate(scr_nonresp_prop = n())%>%
group_by(ssid)%>%
mutate(scr_nonresp_prop = scr_nonresp_prop/n())%>%
ggplot(aes(as.factor(ssid), scr_nonresp_prop))+
geom_bar(stat="summary", fun.y = "mean", alpha = .2, width = .5)
# test<- subset(db_full4new_stim_screen_pupil_nopract, ssid == 321)
table(db_full4new_stim_screen_pupil_nopract_nt$is_scr_nonresponder)
db_full4new_stim_screen_pupil_nopract_nt %>%
subset(is_scr_nonresponder != FALSE)%>%
subset(pp_arousal_outler == FALSE)%>%
subset(!ssid %in% c("304","321", "323", "318","339","331","325"))%>%
# subset( arousal_outler == FALSE )%>%
subset(!is.na(Alexithymia))%>%
group_by(ssid)%>%
mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
# ungroup()%>%
group_by(ssid, Alexithymia,mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
# group_by(ssid)%>%
# mutate(SCR_trend = BIO_CDA.SCR_z[mediansplit_ground_arousal == "Higher arousal"] -
# BIO_CDA.SCR_z[mediansplit_ground_arousal == "Lower arousal"])
# mutate(trendscr = if_else(SCR_trend> 0, "high to high", "high to low"))
# group_by(ssid, Alexithymia,mediansplit_ground_arousal,trendscr)%>%
# summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_sample_arousal2, BIO_CDA.SCR_z))+
stat_summary(geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line')+
# geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
geom_jitter(width = .2, alpha = .2)
facet_grid(~mediansplit_sample_valence2)
# geom_boxplot(alpha = .1)
# geom_text(aes(label = ssid))
facet_grid(~trendscr)
# ylim(-1,1)
304, 318, 339, 331,325
scr_models<- list()
db_full4new_stim_screen_pupil_nopract_nt %>%
subset(is_scr_nonresponder == FALSE)%>%
subset(pp_arousal_outler == FALSE)%>%
subset(!ssid %in% c("304","321", "323", "318","339","331","325"))
# subset( arousal_outler == FALSE )%>%
db_full4new_stim_screen_pupil_nopract_nt_scr_excluding_atypical<- db_full4new_stim_screen_pupil_nopract_nt%>%
subset(!ssid %in% c("304","321", "323", "318","339","331","325"))
# subset(is_scr_nonresponder == FALSE)%>%
group_by(ssid)%>%
mutate(BIO_CDA.PhasicMax_z = scale(BIO_CDA.PhasicMax, center = TRUE, scale = TRUE)[,1])%>%
mutate(valence_z = scale(valence, center = TRUE, scale = TRUE)[,1])%>%
mutate(arousal_z = scale(arousal, center = TRUE, scale = TRUE)[,1])
scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.PhasicMax+.1) ~ valence_z + arousal_z+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = db_full4new_stim_screen_pupil_nopract_nt_scr_excluding_atypical)
scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.PhasicMax+.1) ~ valence_c + arousal_c+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = db_full4new_stim_screen_pupil_nopract_nt)
# subset(!ssid %in% c("304","321", "323", "318","339","331","325"))%>%
subset(is_scr_nonresponder != FALSE))
summary(scr_models$scr_excluding_atypical)
(db_full4new_stim_screen_pupil_nopract_nt%>%
subset(!ssid %in% c("304","321", "323", "318","339","331","325"))))
summary(scr_models$scr_excluding_atypical)db_full4new_stim_screen_pupil_nopract %>%
subset(is_scr_nonresponder == FALSE)%>%
subset(pp_arousal_outler == FALSE)%>%subset( arousal_outler == FALSE )%>% subset(!is.na(Alexithymia))%>% group_by(ssid)%>% mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>% mutate(arousal_z = scale(arousal +.1))%>% ungroup()%>% # group_by(ssid, Alexithymia,mediansplit_self_arousal)%>% # summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(arousal_z, BIO_CDA.SCR_z))+ geom_point()+ # geom_smooth(aes(group = ssid),method = ‘lm’, se = F)+ geom_smooth(method = ‘lm’, se = F)+ facet_grid(~ground_arousal_lab)
# geom_bar(stat="summary", fun.y = "mean", alpha = .2)+geom_jitter(width = .2, alpha = .2)+ ylim(-1,1)
# panel for many folks db_full4new_stim_screen_pupil_nopract %>% # subset(ssid == 314)%>% group_by(ssid)%>% mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>% mutate(arousal_z = scale(arousal +.1))%>% ggplot(aes(arousal_z,BIO_CDA.SCR_z, color =mediansplit_ground_valence , shape =mediansplit_ground_arousal))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ facet_wrap(~ssid)+ ggpubr::stat_cor()
db_full4new_stim_screen_pupil_nopract %>%# subset(ssid == 314)%>% group_by(ssid)%>% mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>% mutate(arousal_z = scale(arousal +.1))%>% ggplot(aes(pup_basCor,BIO_CDA.SCR_z))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ facet_wrap(~ssid)+ ggpubr::stat_cor()
db_full %>%# subset(ssid == 314)%>% group_by(ssid)%>% mutate(BIO_Mean_HR = scale(BIO_Mean_HR, scale = FALSE)[,1])%>% mutate(arousal_z = scale(arousal +.1))%>% ggplot(aes(BIO_Mean_HR,log1p(BIO_CDA.PhasicMax+.1)))+ geom_point()+ geom_smooth(aes(group = ssid),method = ‘lm’, se = F) facet_wrap(~ssid)+ ggpubr::stat_cor()
Let’s try some 3 d visualisations continue here
db_full4new_stim_screen_pupil_nopract_nt %>%
group_by(ssid, mediansplit_ground_arousal, mediansplit_ground_valence,scr_utl)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
subset(scr_utl == "notoutlier")%>%
ggplot(aes(mediansplit_ground_arousal , BIO_CDA.PhasicMax_z))+
geom_jitter(alpha = .1, width = .1)+
stat_summary(geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = "line", alpha = .1)+
facet_grid(~mediansplit_ground_valence)
db_full4new_stim_screen_pupil_nopract_nt%>%
ggplot(aes(BIO_CDA.PhasicMax_z))+
geom_histogram()
db_full4new_stim_screen_pupil_nopract_nt<- db_full4new_stim_screen_pupil_nopract_nt%>%
mutate(scr_utl = if_else(BIO_CDA.PhasicMax_z > 5, "outlier", "notoutlier"))
# + arousal_c
scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.AmpSum_collapsed+.1) ~ arousal_c*valence_c * TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))
summary(scr_models$scr_excluding_atypical)
plot(scr_models$scr_excluding_atypical)
interactions::interact_plot(scr_models$scr_excluding_atypical, pred = arousal_c, modx = valence_c, mod2 = TASc)
db_full4new_stim_screen_pupil_nopract_nt%>%
ggplot(aes())
scr_models$scr1<- lmer(log(BIO_CDA.PhasicMax_collapsed+.1) ~ arousal_c*valence_c * TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))
summary(scr_models$scr1)
scr_models$scr1.2<- lmer(log1p(BIO_CDA.PhasicMax+.1) ~ TASc*arousal_c * valence_c+
(1 | ssid)+
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, Group == "NT", !is.na(Alexithymia)))
unique(db_full4new_stim_screen_pupil_nopract$ssid)
summary(scr_models$scr1.2)
db_full4new_stim_screen_pupil_nopract %>%
group_by(ssid,Group)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
subset(Group == "NT")%>%
ggplot(aes(TAS))+
geom_histogram()
interactions::interact_plot(scr_models$scr1, pred = arousal_c, modx = valence_c, mod2 = TASc)
scr_models$scr1.1<- lmer(CDA.PhasicMax ~ arousal_c*valence_c * TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))
summary(scr_models$scr1.1)
interactions::interact_plot(scr_models$scr1.1, pred = arousal_c, modx = valence_c, mod2 = TASc)
scr_models$scr2<- lmer(BIO_CDA.ISCR_collapsed ~ arousal_c*valence_c *TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))
summary(scr_models$scr2)
interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c)
scr_models$scr2<- lmer(BIO_CDA.SCR_collapsed ~ arousal_c* valence_c *TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract, Group = "NT"))
summary(scr_models$scr2)
interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c)
# the effect of pupil is the opposite on scr
scr_models$scr3<- lmer(BIO_CDA.ISCR_collapsed ~ arousal_c * valence_c * TASc+
(1| ssid) +
(1 | stimIAPS),
REML = FALSE,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))
summary(scr_models$scr3)
interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c, mod2 = TASc)
# the effect of pupil is the opposite on scr
check scr on within individuals
lm_objtest<- lm(BIO_CDA.PhasicMax_z ~ arousal * valence, data = db_full4new_stim_screen_pupil_nopract_nt)
lm_objtest_summ<-summary(lm_objtest)
lm_objtest_summ$coefficients
db_full4new_stim_screen_pupil_nopract_nt
lm_objtest[["coefficients"]][[2]]
lm_objtest[["coefficients"]][["arousal"]]
lm_objtest[["coefficients"]][["valence"]]
regression_df <- data.frame(ssid= rep(NA, 50), Estimate_arousal= rep(NA, 50),
Estimate_valence= rep(NA, 50),
Estimate_int= rep(NA, 50),
p_arousal= rep(NA, 50),
p_valence= rep(NA, 50),
p_int= rep(NA, 50))
lm_ind_summary$coefficients[[14]]
ssid<- unique(db_full4new_stim_screen_pupil_nopract_nt$ssid)
db_full4new_stim_screen_pupil_nopract_nt<- db_full4new_stim_screen_pupil_nopract_nt%>%
group_by(ssid)%>%
mutate(arousal_z_ssid = scale(arousal - mean(arousal, na.rm = TRUE))[,1],
valence_z_ssid = scale(valence - mean(valence, na.rm = TRUE))[,1])
i = 1
rm(b)
# debug
ssid_tes<- unique(db_full4new_stim_screen_pupil_nopract_nt$ssid_num)
table(is.na(db_full4new_stim_screen_pupil_nopract_nt$BIO_CDA.PhasicMax_z))
lm_ind <- lm(BIO_CDA.PhasicMax_z ~ arousal_z_ssid *valence_z_ssid,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) &
ssid_num == ssid_tes[10]))
subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) &
ssid_num == ssid_tes[10])
i = 1
rm(b)
for (b in 5:length(ssid_tes)) {
# for (s in 1:length(nsim)) {
message(sprintf("$$$$$RUNING lmer for ssid %i", ssid_tes[b]))
# db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each = 1920)), replace = FALSE)
#run the model on the current time bin and simulation sumber
lm_ind <- lm(BIO_CDA.PhasicMax_z ~ arousal_z_ssid *valence_z_ssid,
data = subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) &
mediansplit_ground_valence == "More negative"&
ssid_num == ssid_tes[b]))
#store results from the simulation
lm_ind_summary<- summary(lm_ind)
regression_df[i,1]<- ssid_tes[b] #save ssid
regression_df[i,2]<- lm_ind_summary$coefficients[[2]] # arousal beta
regression_df[i,3]<-lm_ind_summary$coefficients[[3]] # valence beta
regression_df[i,4]<-lm_ind_summary$coefficients[[4]] # inyteraction beta
regression_df[i,4]<-lm_ind_summary$coefficients[[4]]
regression_df[i,5]<- lm_ind_summary$coefficients[[14]]
regression_df[i,6]<- lm_ind_summary$coefficients[[15]]
regression_df[i,7]<- lm_ind_summary$coefficients[[16]]
lm_ind_summary$coefficients[[14]]
# simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
# timecourse_result_df_neg[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
# != 0,
# lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')
i = i+1
}
View(regression_df)
lm_ind_summary$coefficients[[4]]
# whether I use ground valence, self valemce or this sample average valence, I get the same patterns
timecourse_result_df_neg%>%
ggplot(aes(timebins, t))+
geom_line(size = 2)+
geom_line(aes(y = p), linetype = "dashed", size = 2)+
geom_hline(yintercept = .1, color = 'red', size = 1.5)
t.test(regression_df$Estimate_arousal)
ssid# just positive
regression_df <- data.frame(ssid= rep(NA, 50), Estimate_arousal= rep(NA, 50),
Estimate_valence= rep(NA, 50),
Estimate_int= rep(NA, 50),
p_arousal= rep(NA, 50),
p_valence= rep(NA, 50),
p_int= rep(NA, 50))
scatter3D(x, y, z, clab = c("Sepal", "Width (cm)"))
db_full4new_stim_screen_pupil_nopract_nt
install.packages("plot3D")
install.packages("misc3d")
library(misc)
plot3D::scatter3D(db_full4new_stim_screen_pupil_nopract_nt$pup_basCor,
db_full4new_stim_screen_pupil_nopract_nt$arousal_c,
db_full4new_stim_screen_pupil_nopract_nt$valence_c)
install.packages("plot3D", dependencies = TRUE)
install.packages("misc3d", repo = 'https://mac.R-project.org')
install.packages(c("rgl", "car"))
library(car)
install.packages("car", dependencies = TRUE)
car::scatter3d(db_full4new_stim_screen_pupil_nopract_nt$arousal_c,
db_full4new_stim_screen_pupil_nopract_nt$pup_basCor,
db_full4new_stim_screen_pupil_nopract_nt$valence_c,
fit = "smooth"
# point = FALSE,
# surface = FALSE,
# point.col = ""
)
gg = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(BIO_CDA.PhasicMax_z, arousal_c)) + stat_density_2d(aes(fill = stat(db_full4new_stim_screen_pupil_nopract_nt$valence_c)), geom = “polygon”, n = 100,bins = 10,contour = TRUE) + # facet_wrap(clarity~.) + scale_fill_viridis_c(option = “A”)
rayshader::plot_gg(gg,multicore=TRUE,width=5,height=5,scale=250)
scr<- db_full4new_stim_screen_pupil_nopract_nt\(BIO_CDA.PhasicMax_z arousal<- db_full4new_stim_screen_pupil_nopract_nt\)arousal_c
valence = db_full4new_stim_screen_pupil_nopract_nt$valence_c
gg = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(BIO_CDA.PhasicMax_z, arousal_c)) + stat_density_2d(aes(fill = stat(valence_c)), geom = “polygon”, # n = 100,bins = 10, contour = TRUE) + # facet_wrap(clarity~.) + scale_fill_viridis_c(option = “A”) rayshader::plot_gg(gg,multicore=TRUE,width=5,height=5,scale=250) gg #No lines pp_nolines = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(x=BIO_CDA.PhasicMax_z, y=arousal_c)) + geom_point(aes(color = valence_c)) scale_fill_viridis_c(a)
rayshader::plot_gg(pp_nolines, width = 4, height = 4, scale = 300, multicore = TRUE) pp_nolines
?scatter3d scatter3d(x = arousal, y = valence, z = scr, fit = “smooth”,
# surface = FALSE,
point.col = "blue",
# point
groups = as.factor(db_full4new_stim_screen_pupil_nopract_nt$mediansplit_sample_valence2),
ellipsoid = FALSE)scatter3d(x = db_full4new_stim_screen_pupil_nopract_nt\(pup_basCor, y = db_full4new_stim_screen_pupil_nopract_nt\)BIO_CDA.PhasicMax_z, z = db_full4new_stim_screen_pupil_nopract_nt, fit = “smooth”,
# surface = FALSE,
point.col = "blue",
# point
groups = as.factor(db_full4new_stim_screen_pupil_nopract_nt$mediansplit_ground_valence),
ellipsoid = FALSE)library(viridisLite) install.packages(“viridis”) library(viridis) deathgg = ggplot(db_full4new_stim_screen_pupil_nopract_nt) + geom_tile(aes(x=arousal_c,y=pup_basCor,fill=valence_c))+ # scale_x_continuous(“Year”,expand=c(0,0),breaks=seq(1900,2010,10)) + # scale_y_continuous(“Age”,expand=c(0,0),breaks=seq(0,100,10),limits=c(0,100)) + scale_fill_viridis() # ggtitle(“Death Probability vs Age and Year for the USA”) + # labs(caption = “Data Source: US Dept. of Social Security”)
rayshader:: plot_gg(deathgg, multicore=TRUE) height=5,width=6,scale=500
heart rate
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
unique(db_full6new$Bio_Mean_HR)
unique(db_full6new$Bio_Mean_HR_fix)
View(db_full6new)
db_full4new_stim_screen_pupil_nopract
db_full4new_stim_screen_pupil_nopract %>%
# subset(ssid == 314)%>%
group_by(ssid)%>%
mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
mutate(arousal_z = scale(arousal +.1))%>%
ggplot(aes(arousal_z,BIO_CDA.SCR_z, color =mediansplit_ground_valence , shape =mediansplit_ground_arousal))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
facet_wrap(~ssid)+
ggpubr::stat_cor()
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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 -->
```r
# keep stim and and fixation
unique(db_full6new$screencontent)
unique(db_full6new$screencontent_no)
db_full6new_hr_fix_stim<- subset(db_full6new, screencontent_no <3)
View(db_full6new_hr_fix_stim)
# fixation" "stim"
# keep controls
unique(db_full6new_hr_fix_stim$ssid)
db_full6new_hr_fix_stim$ssid_num <- as.numeric(as.character(db_full6new_hr_fix_stim$ssid))
db_full6new_hr_fix_stim_nt<- subset(db_full6new_hr_fix_stim, Group == "NT")
unique(db_full6new_hr_fix_stim_nt$ssid_num)
db_full6new_hr_fix_stim_nt<- subset(db_full6new_hr_fix_stim, ssid_num> 303)
unique(db_full6new_hr_fix_stim_nt$ssid)
# HR differences
db_full6new_hr_fix_stim_nt$Bio_Mean_HR_fix<- if_else(db_full6new_hr_fix_stim_nt$screencontent_no ==1, db_full6new_hr_fix_stim_nt$Bio_Mean_HR, NULL)
View(db_full6new_hr_fix_stim_nt)
library(dplyr)
library(tidyverse)
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt %>%
group_by(ssid, tNo) %>%
arrange(ssid, tNo,screencontent_no)%>%
fill(Bio_Mean_HR_fix, .direction = "down")%>%
subset(screencontent_no == 2)
View(db_full6new_hr_fix_stim_nt1)
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1 %>%
mutate(Bio_Mean_HR_dif = Bio_Mean_HR - Bio_Mean_HR_fix)
# 2 1
stim - fix
db_full6new_hr_fix_stim_nt1%>%
ggplot(aes(Bio_Mean_HR_dif))+
geom_histogram()
?scatter3d
db_full6new_hr_fix_stim_nt1%>%
subset(!is.na(pup_basCor))%>%
subset(!is.na(BIO_CDA.PhasicMax_z))%>%
subset(!is.na(Bio_Mean_HR_dif))
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
group_by(ssid)%>%
mutate(BIO_CDA.PhasicMax_z= scale(BIO_CDA.PhasicMax, center = TRUE, scale = TRUE)[,1])
scatter3d(x = db_full6new_hr_fix_stim_nt1$pup_basCor,
y = db_full6new_hr_fix_stim_nt1$BIO_CDA.PhasicMax_z,
z = db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif,
fit = "smooth",
surface = FALSE,
point.col = "blue",
# point
groups = as.factor(db_full6new_hr_fix_stim_nt1$mediansplit_ground_arousal),
ellipsoid = TRUE)
cor(db_full6new_hr_fix_stim_nt1[,c("pup_basCor", "BIO_CDA.PhasicMax_z","Bio_Mean_HR_dif")], use = "complete")
db_full6new_hr_fix_stim_nt1%>%
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(pup_basCor,BIO_CDA.PhasicMax_z, color= mediansplit_ground_valence))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ylim(0,.4)+
ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt1%>%
# group_by(ssid)%>%
# mutate(Bio_Mean_HR_dif_z = scale(Bio_Mean_HR_dif))
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(pup_basCor,Bio_Mean_HR_dif_z, color= mediansplit_ground_valence))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# ylim(-7,2)+
ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt1%>%
subset(!is.na(mediansplit_ground_valence))%>%
# group_by(ssid)%>%
# mutate(Bio_Mean_HR_dif_z = scale(Bio_Mean_HR_dif))
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(BIO_CDA.PhasicMax_z,Bio_Mean_HR_dif, color= mediansplit_ground_valence))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# ylim(-7,2)+
ggpubr::stat_cor()+
facet_grid(~mediansplit_ground_valence)
db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif_outl<- if_else(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif)>
(median(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif),na.rm = TRUE) + (4*(sd(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif), na.rm = TRUE)))), "outlier", "not outlier")
db_full6new_hr_fix_stim_nt1%>%
ggplot(aes(Bio_Mean_HR_dif))+
geom_histogram()+
facet_grid(~Bio_Mean_HR_dif_outl)
db_full6new_hr_fix_stim_nt1 %>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
subset(ssid_num<500)%>%
group_by(ssid)%>%
mutate(Bio_Mean_HR_dif_ssid = mean(Bio_Mean_HR_dif, na.rm = TRUE))%>%
group_by(ssid)%>%
# mutate(Bio_Mean_HR_dif_stim = mean(Bio_Mean_HR_dif, na.rm = TRUE))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(TASc, Bio_Mean_HR_dif))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt1 %>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
subset(ssid_num<500)%>%
# group_by(ssid)%>%
mutate(Bio_Mean_HR_dif = abs(Bio_Mean_HR_dif))%>%
group_by(ssid, stimIAPS)%>%
mutate(Bio_Mean_HR_dif_stim = abs(Bio_Mean_HR_dif))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(TASc, Bio_Mean_HR_dif))+
# geom_point()
geom_smooth(aes(group = stimIAPS, y =Bio_Mean_HR_dif_stim), method=lm, se=FALSE, colour = "gray40", alpha = .01,linetype="dashed", size = .2 )+
geom_smooth( method=lm, se=FALSE, linetype="dashed",
color="darkred", size = 2)+
stat_summary(geom = 'pointrange', pch = 1)+
# geom_point()+
# stat_summary(aes(group =ssid, y = Bio_Mean_HR_dif),method = "line",alpha = .1)+
# ggpubr::stat_cor()+
p$graphstyle+
xlab("Alexithymia (Z)")+
ylab("HR difference")+
p$graphstyle
hr_bio <- lmer(Bio_Mean_HR_dif ~ TASc *arousal_c + (1|ssid) + (1| stimIAPS),
REML = FALSE,
data = subset(db_full6new_hr_fix_stim_nt1, Bio_Mean_HR_dif_outl == "not outlier"))
interactions::interact_plot(hr_bio, pred = arousal_c, modx = TASc)
subset(db_full6new_hr_fix_stim_nt1, Bio_Mean_HR_dif_outl == "not outlier")
db_full6new_hr_fix_stim_nt1 %>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
group_by(ssid)%>%
mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(TAS, cor_hrdif_ar))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
p$graphstyle
db_full6new_hr_fix_stim_nt1 %>%
subset(TAS<70)%>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
group_by(ssid,mediansplit_ground_valence)%>%
mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_valence, cor_hrdif_ar))+
geom_jitter(alpha = .1, width = .1)+
stat_summary(geom = 'pointrange')
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
p$graphstyle
db_full6new_hr_fix_stim_nt1 %>% # subset(Bio_Mean_HR_dif_outl == “not outlier”)%>% group_by(ssid)%>% mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(TAS, cor_hrdif_ar))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
group_by(stimIAPS)%>%
mutate(ValenceMeanThisSample = mean(valence, na.rm = TRUE),
ArousalMeanThisSample = mean(arousal, na.rm = TRUE))
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
ungroup()%>%
mutate(mediansplit_ground_valence = if_else(ValenceMean >=
median(ValenceMean,
na.rm = TRUE), "More positive", "More negative"))%>%
mutate(mediansplit_ground_arousal = if_else(ArousalMean >=
median(ArousalMean,
na.rm = TRUE), "High", "Low"))%>%
mutate(mediansplit_sample_valence2 = if_else(ValenceMeanThisSample >=
median(ValenceMeanThisSample,
na.rm = TRUE), "More positive", "More negative"))%>%
mutate(mediansplit_sample_arousal2 = if_else(ArousalMeanThisSample >=
median(ArousalMeanThisSample,
na.rm = TRUE), "High", "Low"))
db_full6new_hr_fix_stim_nt1%>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
subset(!is.na(mediansplit_ground_valence))%>%
group_by(ssid,mediansplit_ground_arousal, mediansplit_ground_valence,)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif))+
geom_jitter(alpha = .2, width = .2)+
geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
stat_summary(geom = 'pointrange')+
# stat_smooth(aes(group = ssid), se = F, method = 'lm', alpha = .2)+
facet_grid(~mediansplit_ground_valence)
db_full6new_hr_fix_stim_nt1%>%
subset(!is.na(mediansplit_ground_valence))%>%
subset(Bio_Mean_HR_dif_outl== "not outlier")%>%
group_by(ssid, mediansplit_ground_arousal,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif, fill = mediansplit_ground_valence))+
geom_jitter(alpha = .2, width = .2)+
geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
stat_summary(geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', alpha = .2)+
facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_compare_means()
# p$graphstyle+
scale_fill_brewer(palette = "Dark2")+
# stat_smooth(aes(group = ssid), se = F, method = ‘lm’, alpha = .2)
db_full6new_hr_fix_stim_nt1%>% subset(!is.na(mediansplit_ground_valence))%>% subset(Bio_Mean_HR_dif_outl!= “outlier”)%>% group_by(ssid, mediansplit_ground_arousal,mediansplit_ground_valence)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif))+ geom_jitter(alpha = .2, width = .2)+ geom_bar(stat=“summary”, fun.y = “mean”, alpha = .2)+ stat_summary(geom = ‘pointrange’)+ ggpubr::stat_compare_means()+ facet_grid(~mediansplit_ground_valence)
self
db_full6new_hr_fix_stim_nt1%>% subset(!is.na(mediansplit_ground_valence))%>% subset(Bio_Mean_HR_dif_outl!= “outlier”)%>% group_by(ssid, mediansplit_sample_arousal2,mediansplit_sample_valence2)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(mediansplit_sample_arousal2, Bio_Mean_HR_dif))+ geom_jitter(alpha = .2, width = .2)+ stat_summary(aes(group = ssid), geom = ‘line’, alpha = .1, size = .4, colour = “Black”) + # geom_bar(stat=“summary”, fun.y = “mean”, alpha = .2)+ stat_summary(geom = ‘pointrange’)+ ggpubr::stat_compare_means()+ facet_grid(~mediansplit_sample_valence2)
lmer( db_full6new_hr_fix_stim_nt1)
db_full6new_hr_fix_stim_nt1%>% group_by(ssid)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(arousal))+ geom_histogram()
# test - analyse just positive
hr_dif_models$arousal_val <- lmer(Bio_Mean_HR_dif ~ valence_c *arousal_c+
+ (1 | ssid) + (1|stimIAPS),
# (1|mediansplit_sample_arousal2),
REML = FALSE,
data = subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" & Bio_Mean_HR_dif_outl == "not outlier" & Group == "NT"))
summary(hr_dif_models$arousal_val)
unique(db_full6new_hr_fix_stim_nt1$ssid)
hr_dif_models$valfrom_hr <- lmer(valence_c ~ Bio_Mean_HR_dif* arousal_c+
+ (1 | ssid) + (1|stimIAPS),
# (1|mediansplit_sample_arousal2),
REML = FALSE,
data = subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" &
Bio_Mean_HR_dif_outl == "not outlier" & Group == "NT"))
arousal_c
summary(hr_dif_models$valfrom_hr)
interactions::interact_plot(hr_dif_models$valfrom_hr, pred = Bio_Mean_HR_dif, modx = arousal_c)
plot(hr_dif_models$arousal_val)
car::vif(hr_dif_models$arousal_val)
# valence_c arousal_c valence_c:arousal_c
# 1.767605 1.708642 1.070635
?step
?lmerTest::step()
lmerTest::step(hr_dif_models$arousal_val)
# favours valence
# check the response bias thing
db_full6new_hr_fix_stim_nt1 %>%
subset(!is.na(mediansplit_ground_valence))%>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
ggplot(aes(log10(arousal+10.5) ,Bio_Mean_HR_dif))+
# geom_point(alpha = .1)+
facet_grid(~mediansplit_ground_valence)+
geom_smooth(method = 'lm', se = F)+
ggside::geom_ysidedensity(aes(x=stat(density, fill = mediansplit_self_valence), alpha = .4))+
ggside::geom_xsidedensity(aes(y=stat(density, fill = mediansplit_self_valence), alpha = .4))
scatter3d(x = db_full6new_hr_fix_stim_nt1$valence_c,
y =db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif, z =db_full6new_hr_fix_stim_nt1$arousal_c,
# fit = "smooth",
# surface = FALSE,
point.col = "blue",
# point
# ellipsoid = db_full6new_hr_fix_stim_nt1$mediansplit_sample_valence2)
ellipsoid = TRUE)
db_full6new_hr_fix_stim_nt1%>%
group_by(stimIAPS, mediansplit_ground_valence, mediansplit_ground_arousal)%>%
ggplot(aes(mediansplit_ground_valence, ValenceMean))+
geom_point()
db_full6new_hr_fix_stim_nt1%>%
subset(Condition == "IAPS")%>%
group_by(stimIAPS, mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_valence, arousal))+
stat_summary(geom = 'pointrange')+
ggpubr::stat_compare_means()+
geom_text(aes(label = stimIAPS))
check corelation between arousal and valence on and individual basis
db_full6new_hr_fix_stim_nt1$arousal_c
subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" & Bio_Mean_HR_dif_outl == "not outlier") %>%
group_by(ssid)%>%
mutate(cor_vale_ar = cor(arousal, valence, use = "complete"))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(cor_vale_ar))+
geom_histogram()+
geom_vline(xintercept = -.7)
subset(db_full6new_hr_fix_stim_nt1)%>%
# group_by(mediansplit_ground_valence, stimIAPS)%>%
# summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes( valence, arousal))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# geom_smooth(aes(group = ssid), se = F, method = 'lm')+
# facet_grid(~mediansplit_ground_valence)+
ggpubr::stat_cor()
subset(db_full6new_hr_fix_stim_nt1)%>%
subset(ssid == 331)%>%
# group_by(mediansplit_ground_valence, stimIAPS)%>%
# summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes( as.numeric(tNo), arousal))+
geom_point()+
stat_summary(geom = "line")+
geom_point(aes(y = valence), color = "green")+
stat_summary(aes(y = valence),geom = "line", linetype = "dashed", color = "green")
# geom_smooth(method = 'lm', se = F)+
# geom_smooth(aes(group = ssid), se = F, method = 'lm')+
# facet_grid(~mediansplit_ground_valence)+
# ggpubr::stat_cor()
try and regress valence our oarousal_val
db_full6new_hr_fix_stim_nt2
db_full6new_hr_fix_stim_nt3<- subset(db_full6new_hr_fix_stim_nt2, !is.na(valence))
hr_dif_models$arosal_from_val <- lmer(arousal_c~ valence_c + + (1 | ssid) + (1|stimIAPS), # (1|mediansplit_sample_arousal2), REML = FALSE, data = subset(db_full6new_hr_fix_stim_nt3))
hr_dif_models$val_from_arousal <- lmer(valence_c~arousal_c+ + (1 | ssid) + (1|stimIAPS), # (1|mediansplit_sample_arousal2), REML = FALSE, data = subset(db_full6new_hr_fix_stim_nt3))
hr_dif_models\(arosal_from_val plot(hr_dif_models\)arosal_from_val)
?residuals ?resid ?residuals.merMod db_full6new_hr_fix_stim_nt3\(arous_val_resid <- residuals(hr_dif_models\)arosal_from_val)
db_full6new_hr_fix_stim_nt3\(val_from_arous_resid<- residuals(hr_dif_models\)val_from_arousal)
residuals.merMod(hr_dif_models$arosal_from_val)
ar_frm_val<- lm(arousal_c ~ valence_c, data = db_full6new_hr_fix_stim_nt3)
val_frm_val<- lm( valence_c~arousal_c, data = db_full6new_hr_fix_stim_nt3)
db_full6new_hr_fix_stim_nt3%>%ggplot(aes(val_from_arous_resid, arous_val_resid))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ ggpubr::stat_cor()
resid(ar_frm_val)
resid(val_frm_val)
# 1 2 3 4 5 6 7 8
# 0.149962843 0.505815507
#
# -0.637512958 0.044580602
Is the residual of A~ B the same as B~A
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<!-- rnb-source-begin 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 -->
```r
hr_dif_models$val_from_arousal <- lmer(valence_c~arousal_c+
+ (1 | ssid) + (1|stimIAPS),
# (1|mediansplit_sample_arousal2),
REML = FALSE,
data = subset(db_full6new_hr_fix_stim_nt3))
hr_dif_models$biohr_dif_arous_res <- lmer(Bio_Mean_HR_dif~ val_from_arous_resid *arous_val_resid+
+ (1 | ssid) + (1|stimIAPS),
# (1|mediansplit_sample_arousal2),
REML = FALSE,
data = subset(db_full6new_hr_fix_stim_nt3, Bio_Mean_HR_dif_outl == "not outlier"))
summary(hr_dif_models$biohr_dif_arous_res)
# analyses of residuals also suggets that effects on bio_hr are related to common arousal and valence relation
interactions::interact_plot(hr_dif_models$arousal_val, pred= arousal_c, modx = valence_c)
alexithjymia
db_full6new_hr_fix_stim_nt1\(Bio_Mean_HR_dif_z<- scale(db_full6new_hr_fix_stim_nt1\)Bio_Mean_HR_dif, center = TRUE, scale = TRUE)[,1] hr_dif_models$arousal_val_cat <- lmer(Bio_Mean_HR_dif_z ~TASc*arousal_c+ + (1 | ssid) + (1|stimIAPS), # (1|mediansplit_sample_arousal2), REML = FALSE, data = db_full6new_hr_fix_stim_nt1)
interactions::interact_plot(hr_dif_models$arousal_val_cat, pred= arousal_c, modx = TASc)
summary(hr_dif_models$arousal_val_cat)
plot(hr_dif_models\(arousal_val) plot(hr_dif_models\)arousal_val_cat)
summary(hr_dif_models$arousal_val_cat)
hr_dif_models$arousal_val_cat <- lmer(Bio_Mean_HR_dif~ mediansplit_sample_arousal2* mediansplit_sample_valence2+
+ (1+mediansplit_sample_valence2 | ssid) + (1|stimIAPS),
# (1|mediansplit_sample_arousal2),
REML = FALSE,
data = db_full6new_hr_fix_stim_nt1)
summary(hr_dif_models$arousal_val_cat)
interactions::cat_plot(hr_dif_models$arousal_val_cat, pred = mediansplit_sample_arousal2, modx = mediansplit_sample_valence2)
correlation
db_full6new_hr_fix_stim_nt1%>%
subset(!is.na(mediansplit_ground_valence))%>%
subset(Bio_Mean_HR_dif_outl!= "outlier")%>%
group_by(ssid,mediansplit_ground_valence, mediansplit_ground_arousal)%>%
mutate(cor_hr_arous = cor(Bio_Mean_HR_dif, arousal, use = "complete"))%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(mediansplit_ground_valence, cor_hr_arous))+
# geom_jitter(alpha = .2, width = .2)+
geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
stat_summary(geom = 'pointrange')+
# geom_smooth(aes(group = ssid), color = 'gray', method = 'lm', se = F)+
ggpubr::stat_compare_means()+
facet_grid(~mediansplit_ground_arousal)
# xlim(-5,3)
db_full6new_hr_fix_stim_nt1 %>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(Age, TASc))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
hr_dif_models<- list()
hr_dif_models$arousal_val <- lmer(Bio_Mean_HR_dif~ (arousal_c*valence_c )* TASc +(1+arousal_c | ssid) + (1|stimIAPS),
REML = FALSE,
data = db_full6new_hr_fix_stim_nt1)
summary(hr_dif_models$arousal_val)
interactions::interact_plot(hr_dif_models$arousal_val, pred = arousal_c, modx = TASc)
hr_dif_models$arousal_val <- lmer(Bio_Mean_HR~ arousal_c* valence_c +(1+arousal_c| ssid),
# + (1|stimIAPS),
REML = FALSE,
data = db_full6new_hr_fix_stim_nt1)
summary(hr_dif_models$arousal_val)
# plot(hr_dif_models$arousal_val)
interactions::interact_plot(hr_dif_models$arousal_val, pred = valence_c, modx = arousal_c)
import HRV resting
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```r
Rest_HRV <- read_excel("Rest HRV.xlsx")
View(Rest_HRV)
Rest_HRV%>%
ggplot(aes(RMSSD))+
geom_histogram()
Rest_HRV%>%
ggplot(aes(RMSSD))+
geom_histogram()
Rest_HRV%>%
ggplot(aes(MEANHR))+
geom_histogram()
BIO_HRVanalysys$ssid<- as.character(BIO_HRVanalysys$ssid)
left_join(BIO_HRVanalysys, Rest_HRV, by = "ssid")%>%
ggplot(aes(RMSSD, RMMSSD))+
geom_point()+
ggpubr::stat_cor()
# library(readxl)
Rest_practice_HRV <- read_excel("Rest + practice HRV.xlsx")
View(Rest_practice_HRV)
Rest_practice_HRV$ssid<- as.character(Rest_practice_HRV$ssid)
left_join(Rest_practice_HRV, BIO_HRVanalysys, by = "ssid")%>%
ggplot(aes(RMSSD, RMMSSD))+
geom_point()+
ggpubr::stat_cor()
left_join(Rest_practice_HRV, Rest_HRV, by = "ssid")%>%
ggplot(aes(RMSSD.x, RMSSD.y))+
geom_point()+
ggpubr::stat_cor()
Rest_practice_HRV%>%
ggplot(aes(MEANHR))+
geom_histogram()
# import hrv task
library(readxl)
hrvResultsTask <- read_excel("Summary_hrvResultsTask.xls")
View(hrvResultsTask)
combine with data
Rest_HRV
Rest_HRV$subject<- NULL
nrow(db_full6new_hr_fix_stim_nt1)
# 2160
db_full6new_hr_fix_stim_nt2<- left_join(db_full6new_hr_fix_stim_nt1, Rest_HRV)
# hrv rest vs hrtaskCallbackManager(
left_join(Rest_HRV, hrvResultsTask, by = "ssid") %>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(RMSSD.x, RMSSD.y))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
xlim(0,100)+
ylim(0,100)
Rest_HRV$hrvcondition<- "rest"
hrvResultsTask$hrvcondition<- "task"
left_join(Rest_HRV, hrvResultsTask, by = "ssid") %>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(RMSSD.x, RMSSD.y))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()+
xlim(0,100)+
ylim(0,100)
colnames(hrvResultsTask)
bind_rows(Rest_HRV, hrvResultsTask)%>%
subset(RMSSD<100)%>%
ggplot(aes(hrvcondition, hfpower))+
geom_jitter(width = .1, alpha = .1)+
stat_summary(geom = 'pointrange')+
stat_summary(aes(group = ssid), geom = 'line', alpha = .1)left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
group_by(ssid)%>%
mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(NN50,cor ))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
xlim(0,500)+
ggpubr::stat_cor()
left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
group_by(ssid)%>%
mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(TASc, NN50))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# ylim(0,200)+
ggpubr::stat_cor()
hrvResultsTask%>%
ggplot(aes(RMSSD))+
geom_histogram()
left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
subset(!is.na(mediansplit_ground_valence))%>%
# group_by(ssid)%>%
# mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(RMSSD,pup_basCor,color = mediansplit_ground_valence ))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# xlim(0,100)+
ggpubr::stat_cor()
left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
subset(!is.na(mediansplit_ground_valence))%>%
# group_by(ssid)%>%
# mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(RMSSD,BIO_CDA.PhasicMax_z,color = mediansplit_ground_valence ))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
xlim(0,100)+
ggpubr::stat_cor()
left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
subset(!is.na(mediansplit_ground_valence))%>%
subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
# group_by(ssid)%>%
# mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
group_by(ssid,mediansplit_ground_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(`LF/HF`,BIO_CDA.PhasicMax_z,color = mediansplit_ground_valence ))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
# xlim(0,5000)+
ggpubr::stat_cor()db_full6new_hr_fix_stim_nt2 %>% group_by(ssid)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(Bio_Mean_HR_dif, lfpower))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt2 %>% group_by(ssid)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(MEANHR, RMSSD))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ ggpubr::stat_cor()
db_full6new_hr_fix_stim_nt2 %>% group_by(ssid)%>% summarise_if(is.numeric, mean, na.rm = TRUE)%>% ggplot(aes(MEANHR, Bio_Mean_HR))+ geom_point()+ geom_smooth(method = ‘lm’, se = F)+ ggpubr::stat_cor()
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin 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ZCkpPDUwMCAmQklPX0NEQS5BbXBTdW0+IC4xKSkpXG5cblxuXG5cblxuXG5gYGAifQ== -->
```r
# work on this
db_full6new_304_up$
colnames(db_full4new_stim_screen_pupil_nopract)
colnames(db_full6new_304_up)
# db_full6new
unique(db_full6new_304_up$screencontent)
View(db_full6new)
# store heart rate baseline to compute difference
db_full6new$Bio_Mean_HR_fix = if_else(db_full6new$screencontent == "fixation", db_full6new$Bio_Mean_HR, NULL)
# compute heart rate difference to fixation screen
db_full6new_304_up$Bio_Mean_HR
View(db_full6new_304_up)
db_full6new_304_up<- db_full6new_304_up%>%
group_by(ssid)%>%
mutate(BIO_CDA.PhasicMax_z = scale(BIO_CDA.PhasicMax)[,1])
pupulresidtest<- lmer(pup_basCor ~ BRIGHTNESSc +(1+BRIGHTNESSc|ssid), REML = FALSE,
data = testresiddf)
summary(pupulresidtest)
plot(pupulresidtest)
resid(pupulresidtest)
tstresid <- resid(pupulresidtest)
testresiddf$puopil_resid <- tstresid
testresiddf<- subset(db_full6new_304_up, !is.na(pup_basCor))
summary(lmer(arousal ~ pupil_resid + (1|ssid), REML = FALSE,
data = testresiddf))
summary(lmer(arousal ~ puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax + (1|ssid),
# (0+puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax|ssid),
REML = FALSE,
data = subset(testresiddf, as.numeric(as.character(ssid))<500)))
subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
group_by(ssid)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(arousal, BIO_CDA.PhasicMax))+
geom_point()+
geom_smooth(method = 'lm', se = F)+
ggpubr::stat_cor()
subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
mutate(median_ground_arousal = if_else(ArousalMean> median(ArousalMean, na.rm = TRUE), "High", "Low"))%>%
mutate(median_ground_valence = if_else(ValenceMean> median(ValenceMean, na.rm = TRUE), "More positive", "More negative"))%>%
group_by(ssid)%>%
ungroup()%>%
mutate(median_self_arousal = if_else(arousal> median(arousal), "High", "Low"))%>%
mutate(median_self_valence = if_else(valence> median(valence), "Positive", "Negative"))%>%
group_by(ssid,median_self_arousal,median_self_valence)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
ggplot(aes(median_self_arousal, puopil_resid,))+
geom_jitter(width = .2, alpha = .2)+
geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
stat_summary(geom = 'pointrange')+
facet_grid(~median_self_valence)
ggpubr::stat_cor()
subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
mutate(median_ground_arousal = if_else(ArousalMean> median(ArousalMean, na.rm = TRUE), "High", "Low"))%>%
mutate(median_ground_valence = if_else(ValenceMean> median(ValenceMean, na.rm = TRUE), "More positive", "More negative"))%>%
group_by(ssid)%>%
mutate(median_self_arousal = if_else(arousal> median(arousal), "High", "Low"))%>%
mutate(median_self_valence = if_else(valence> median(valence), "Positive", "Negative"))%>%
group_by(ssid,median_self_arousal)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
subset(!is.na(median_self_arousal))%>%
ggplot(aes(median_self_arousal, BIO_CDA.PhasicMax_z))+
geom_jitter(width = .2, alpha = .2)+
geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
# geom_smooth(aes(group = ssid),method = 'lm', se = F)+
# geom_text(aes(label = ssid))
stat_summary(geom = 'pointrange')
facet_grid(~median_self_valence)
ggpubr::stat_cor()
summary(lmer(arousal ~ log(BIO_CDA.PhasicMax+.1) + (1|ssid) + (1+log(BIO_CDA.PhasicMax+.1)|stimIAPS),
# (0+puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax|ssid),
REML = FALSE,
data = subset(testresiddf, as.numeric(as.character(ssid))<500 &BIO_CDA.AmpSum> .1)))
---
title: "R Notebook"
output: html_notebook
---



Downsample data to save space

pupiltimecourse.data is dowsnampled but doesn't have all participants < 400

but the script pupiltimecourseexp2.Rmd has what we need to do the same dowsnampling

Goal
- take pupil timecourse, dowsnample and then mrge it it behavioural data so that we can do the additional arousal pupil analyses that check for timecourse things.



check gazeR or downsampling


to do, do the same arousal correlation with valence
```{r}
library(ggplot2)
library(tidyverse)
install.packages("gazer")
library(gazer)

?gazer::downsample_gaze
?gazer::downsample_pupil


unique(X_tmp_df4_full$Condition)
# gazer::downsample_gaze%

tmp.df4_full_stim<- X_tmp_df4_full %>%
  subset(screencontent == "stim")


# create bins
# install.packages("OneR")
install.packages("OneR")
library(OneR)

?bin


tmp.df4_full_stim$timebin<- OneR::bin(tmp.df4_full_stim$timerezero3, nbin = 60)



# dowsample by averaging?
colnames(tmp.df4_full_stim)

tmp.df4_full_stim_downs
tmp.df4_full_stim_downs <- tmp.df4_full_stim %>%
  group_by(ssid, tNo, timebin, trial,trialUnq)%>%
  summarise_at(c('timerezero3','pup_basCor', 'gaze_x_cor_pix', 'gaze_y_cor_pix'), 
               mean, na.rm = TRUE)


?saveRDS
saveRDS(tmp.df4_full_stim_downs, "tmp.df4_full_stim_downs.rds")

# create klarger bins

tmp.df4_full_stim_downs_jun2021
tmp.df4_full_stim_downs_jun2021_with_beh

range(tmp.df4_full_stim_downs$timerezero3)

tmp.df4_full_stim_downs_jun2021_with_beh$timebin2 <- OneR::bin(tmp.df4_full_stim_downs_jun2021_with_beh$timerezero3, nbin = 30)

tmp.df4_full_stim_downs_jun2021_with_beh2 <- tmp.df4_full_stim_downs_jun2021_with_beh %>%
  group_by(ssid, tNo, timebin2, trial,trialUnq)%>%
  summarise_at(c('timerezero3','pup_basCor', 'gaze_x_cor_pix', 'gaze_y_cor_pix'), 
               mean, na.rm = TRUE)

nrow(tmp.df4_full_stim_downs_jun2021_with_beh2)
87529
```


pupil arousal

```{r}
library(gghalves)

# pupil tracks arousal

db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, ground_arousal_lab, Alexithymia, mediansplit_ground_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ground_arousal_lab, pup_basCor, color = ground_arousal_lab,fill = ground_arousal_lab))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")
  facet_grid(~mediansplit_ground_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)
  
  
  
  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, mediansplit_self_arousal, Alexithymia, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_self_arousal, pup_basCor, color = mediansplit_self_arousal,fill = mediansplit_self_arousal))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  # xlab("Arousal")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)
  
mediansplit_self_arousal_thissample
  
  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, mediansplit_self_arousal_thissample, Alexithymia, mediansplit_self_valence_thissample)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_self_arousal_thissample, pup_basCor, color = mediansplit_self_arousal_thissample,fill = mediansplit_self_arousal_thissample))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  # xlab("Arousal")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence_thissample)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)
  
  
  db_full4new_stim_screen_pupil_nopract%>%
    
     ggplot(aes( valence,arousal))+
    geom_point()+
  stat_smooth(method = "lm", formula = y ~ x + I(x^2), size = 1)+
    facet_grid(~mediansplit_self_arousal)
    # geom_smooth(aes(x = arousal^2, y = valence))
   db_full4new_stim_screen_pupil_nopract%>%
    
    ggplot(aes( valence,arousal))+
    geom_point()+
  stat_smooth(method = "lm", formula = y ~ x + I(x^2), size = 1)+
    facet_grid(~mediansplit_self_valence)
   
   
   unique(db_full4new_stim_screen_pupil_nopract$ssid)
```
  
```{r}
# mediansplit_self_valence is duoliating the stimuli based ion peopl who choose negative and positive
unique(db_full4new_stim_screen_pupil_nopract)
db_full4new_stim_screen_pupil_nopract <- db_full4new_stim_screen_pupil_nopract%>%
  ungroup()%>%
  # group_by(stimIAPS)%>%
  mutate(mediansplit_sample_valence2 = if_else(ValenceMeanThisSample >=
                                                  median(ValenceMeanThisSample,
                                                         na.rm = TRUE), "More positive", "More negative"))%>%
  mutate(mediansplit_sample_arousal2 = if_else(ArousalMeanThisSample >=
                                                  median(ArousalMeanThisSample,
                                                         na.rm = TRUE), "High", "Low"))
  
  
  group_by(mediansplit_sample_valence2, stimIAPS,stimDescription)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

```

  
  
brightness

```{r}
tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  group_by(stimIAPS)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ValenceMean, Mean_gray_z))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()


tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  group_by(stimIAPS)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ArousalMean, Mean_gray_z))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()


tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  group_by(stimIAPS,ground_valence_lab)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ground_valence_lab, Mean_gray_z))+
  geom_point()+
  stat_summary(geom = 'pointrange')
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  
  
  tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  group_by(stimIAPS,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, Mean_gray_z))+
  # geom_point()+
  stat_summary(geom = 'pointrange')
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  
  
  
    tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  group_by(stimIAPS,ground_valence_lab)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(Mean_gray_z, color = ground_valence_lab))+
      # geom_histogram()+
      geom_density()
  # geom_point()+
  stat_summary(aes(group = mediansplit_ground_valence),geom = 'line')
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  
  
  
  
  # 
  
tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  group_by(stimIAPS,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, ArousalMean))+
      # geom_histog `vram()
      # geom_density()
  # geom_point()+
  stat_summary(geom = 'pointrange')
  # geom_smooth(method = 'lm', se = F)+
  # ggpubr::stat_cor()
      
      
      ground_valence_lab
```

  
Participants section

```{r}
participants_demog <- db_full4new_stim_screen_pupil_nopract %>%
    # subset(pp_arousal_outler== FALSE)%>%
  subset(Group == "NT")%>%
  group_by(ssid, Gender)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)

unique(participants_demog)

# gender
table(participants_demog$Gender)


colnames(participants_demog)
psych::describe(participants_demog[c("Age")])

summary(participants_demog$Age)

sd(participants_demog$Age, na.rm = TRUE)



table(is.na(participants_demog$Age))


participants_demog %>%
  group_by(Gender)%>%
  mutate(genderspecif_age = sd(TAS))%>%
  ungroup()%>%
  mutate(nongenderspecif_age = sd(TAS))%>%
  group_by(Gender)%>%
  summarise_at(c("genderspecif_age","nongenderspecif_age"), mean, na.rm = TRUE)


(10.557140+8.042743)/2

8.042743

9.299941

```



  
  
```{r}
  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(Group == "NT")%>%
  # subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, mediansplit_ground_arousal, Alexithymia, mediansplit_ground_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_arousal, pup_basCor, color = mediansplit_ground_arousal,fill = mediansplit_ground_arousal))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  # xlab("Arousal")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_ground_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)
```

  
  
```{r}
  
    db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, mediansplit_ground_arousal, Alexithymia, mediansplit_ground_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_arousal, Mean, color = mediansplit_ground_arousal,fill = mediansplit_ground_arousal))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  # xlab("Arousal")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_ground_valence)+
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(90,130)
  
  
```
  
  
  okay let's create averages frio this entire sample and use those to devide the stimuli
  
  self aropusal might be biased by problematic folks but averages for the entire sample should not
  
```{r}

db_full4new_stim_screen_pupil_nopract %>%
  group_by(stimIAPS)%>%
  mutate()


  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid_num< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, self_arousal_lab, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(self_arousal_lab, pup_basCor, color = self_arousal_lab,fill = self_arousal_lab))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  # xlab("Arousal")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)
  
  
sample_iaps_arousal<-  db_full4new_stim_screen_pupil_nopract %>%
    
    group_by(stimIAPS)%>%
    subset(ssid_num< 500)%>%
    summarise_at(c('ArousalMeanThisSample','ArousalMean'), mean, na.rm = TRUE)%>%
    ggplot(aes(ArousalMeanThisSample, ArousalMean))+
    geom_point()+
    geom_smooth(method = 'lm', se = F)+
    ggpubr::stat_cor()+
    theme_classic()
  
  sample_iaps_val<-   db_full4new_stim_screen_pupil_nopract %>%
    
     group_by(stimIAPS)%>%
    subset(ssid_num< 500)%>%
    summarise_at(c('ValenceMeanThisSample','ValenceMean'), mean, na.rm = TRUE)%>%
    ggplot(aes(ValenceMeanThisSample, ValenceMean))+
    geom_point()+
    geom_smooth(method = 'lm', se = F)+
    ggpubr::stat_cor()+
      theme_classic()

library(patchwork)
sample_iaps_arousal+
sample_iaps_val
```


  

  
```{r} 
  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid_num< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, self_arousal_lab, Alexithymia, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(self_arousal_lab, pup_basCor, color = self_arousal_lab,fill = self_arousal_lab))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)



db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid_num< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, ground_arousal_lab, Alexithymia, mediansplit_ground_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ground_arousal_lab, pup_basCor, color = ground_arousal_lab,fill = ground_arousal_lab))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")
  facet_grid(~mediansplit_ground_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)


```

Resposne bias
```{r}
library(ggExtra)
arous_pup_by_val<- db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(ssid, stimIAPS, mediansplit_self_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, pup_basCor))+
      geom_point(alpha = .2)+
    # stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)
  # ggpubr::stat_cor()
  ylim(-3.3,1.3)

  arous_pup_by_val<- arous_pup_by_val+ggside::geom_xsidedensity(aes(y=stat(density),fill = mediansplit_self_valence), alpha = .4)
    
  arous_pup_by_val+ggside::geom_ysidedensity(aes(x=stat(density),fill = mediansplit_self_valence), alpha = .4)
  
  
ggMarginal(arous_pup_by_val, type = "density")
```

ceiliing effect?

```{r}
db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(ssid, stimIAPS, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, pup_basCor))+
      geom_point()+
    # stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()+
  ylim(-3.3,1.3)


```

ceiling effect?
```{r}
db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(mediansplit_ground_valence)%>%
  mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  group_by(mediansplit_ground_valence)%>%
  mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, pup_basCor))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .4, size = 1, colour = "Black") +
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
```
  
  
```{r}  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(mediansplit_ground_valence)%>%
  mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  group_by(mediansplit_ground_valence)%>%
  mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, pup_basCor))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .4, size = 1, colour = "Black") +
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_self_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_self_valence, pup_basCor))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
```


```{r}

# db_full4new_stim_screen_pupil_nopract$arousal_outler
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(ssid, mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_sample_arousal2, arousal))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_sample_valence2)+
  ggpubr::stat_cor()


# stim
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(stimIAPS, mediansplit_self_valence, mediansplit_self_arousal)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_self_arousal, arousal))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
  # stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)+
  ggpubr::stat_cor()


  # ground
  
  # stim
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  group_by(stimIAPS, mediansplit_ground_arousal, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_arousal, ArousalMean))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary( geom = 'pointrange', alpha = .5, size = 1, colour = "Black") +
  # stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()


```
  
  
    


otehr confounds - eye movements

```{r}
db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, mean_fix_dur))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
   unique(db_full4new_stim_screen_pupil_nopract$ValenceMeanThisSample)
   
   
   median(db_full4new_stim_screen_pupil_nopract$ValenceMeanThisSample)
    median(db_full4new_stim_screen_pupil_nopract$valence)
    
    
     median(db_full4new_stim_screen_pupil_nopract$arousal)
    median(db_full4new_stim_screen_pupil_nopract$ArousalMeanThisSample)
  
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_self_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_self_valence, mean_fix_dur))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
```

```{r}
  # data loss gaze
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, fix_count))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, SacAmp))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
  
  db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, sdevgaze))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
 db_full4new_stim_screen_pupil_nopract %>%
  subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, gaze_loss_prop))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  ylim(-3.3,1.3)
  
  
  
  db_full4new_stim_screen_pupil_nopract%>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_mean = mean(pup_basCor, na.rm = TRUE), sd_pup = sd(pup_basCor, na.rm = TRUE), subj = n())%>%
  group_by(ssid, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # group_by(mediansplit_ground_valence)%>%
  # mutate(pup_basCor_se = sd_pup/(sqrt(subj)))%>%
  ggplot(aes(mediansplit_ground_valence, total_fix_dur))+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
  stat_summary(geom = 'pointrange', alpha = .1, size = 1, colour = "Black") +
  # geom_errorbar(aes(ymin = pup_basCor_mean-pup_basCor_se, ymax = pup_basCor_mean+pup_basCor_se ))+
  geom_smooth(method = 'lm', se = F)+
  p$graphstyle+
  xlab("Valence")+
  # ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()
  
  
```

# let's model it formally
  
# remember baseline corection is equivalendt to stimuli intercept
 
```{r} 
pupil_arousal_findings<- list()
library(lmerTest)

unique(db_full4new_stim_screen_pupil_nopract$stimIAPS)
unique(substr(db_full4new_stim_screen_pupil_nopract$stimIAPS, 1,8))
db_full4new_stim_screen_pupil_nopract$Label<- substr(db_full4new_stim_screen_pupil_nopract$stimIAPS, 1,8)
  
db_full4new_stim_screen_pupil_nopract<- left_join(db_full4new_stim_screen_pupil_nopract, imageJ_IAPS, by = "Label")
nrow(db_full4new_stim_screen_pupil_nopract)
# rows = 2858

db_full4new_stim_screen_pupil_nopract$Mean_gray_z<- scale(db_full4new_stim_screen_pupil_nopract$Mean, center = TRUE, scale = TRUE)[,1]

options(contrasts = c("contr.sum","contr.poly"))
options(scipen = 999)

db_full4new_stim_screen_pupil_nopract$ssid_num <- as.numeric(as.character(db_full4new_stim_screen_pupil_nopract$ssid))

pupil_arousal_findings$pupil_from_arousal <- lmer(pup_basCor ~ arousal_c*Mean_gray_z  +(1 +Mean_gray_z | ssid) + (0+arousal_c| ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 & 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_arousal)
# singularity (drop arousal c slope)

# is there a coorelationbtween mean arousal rating and meagray(

db_full4new_stim_screen_pupil_nopract%>%
  group_by(stimIAPS,ground_valence_lab) %>%
  summarise_at(c('arousal_c', 'Mean_gray_z'), mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, Mean_gray_z, color = ground_valence_lab))+
  geom_point()+
  ggpubr::stat_cor()


# <- predict(pupil_arousal_findings$pupil_from_arousal)
db_full4new_stim_screen_pupil_nopract %>%
  group_by(stimIAPS) %>%
  # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  # summarise_at(c('arousal_c', 'Mean_gray_z'), mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, pup))+
  geom_smooth(aes(group = ssid, alpha = ssid), method = 'lm', se = F)
# geom_smooth(aes(group = stimIAPS), method = 'lm', se = F)
  ggpubr::stat_cor()


```
cor.test(db_full4new_stim_screen_pupil_nopract$, db_full4new_stim_screen_pupil_nopract$)


```{r}
# if we put brighteness and arousal we see no interaction which means we probably don't need to worry about brighteness, it doesn't change effects of arousal self report
# db_full4new_stim_pupil$pup
pupil_arousal_findings$pupil_from_arousal <- lmer(pup_basCor ~ arousal_c+Mean_gray_z  +(1 +Mean_gray_z | ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract$pup, pupil_outlier == FALSE & ssid_num< 500& 
                                      arousal_outler == FALSE))

install.packages('usdm')

usdm::vif(as.data.frame(db_full4new_stim_screen_pupil_nopract[,c('arousal', 'valence')]))

pupil_arousal_findings$pupil_from_arousal_withval <- lmer(pup_basCor ~ arousal_c*valence_c* Mean_gray_z  +(1 +Mean_gray_z | ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500& 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_arousal_withval)
car::vif(pupil_arousal_findings$pupil_from_arousal_withval)

lmerTest::step(pupil_arousal_findings$pupil_from_arousal_withval)



# control for baseline arousal ratings

pupil_arousal_findings$pupil_from_arousal_withval

summary(lmer(pup_basCor ~ arousal_c*valence_c+ Mean_gray_z  +(1 | ssid) + (1|stimIAPS),
               # (1|ArousalMean)+ (1|ArousalMean), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & Group == "NT"  & 
                                      arousal_outler == FALSE)))



# arousal_c:valence_c                      0  35.52   35.52     1 2150.19  13.5760 0.0002348 ***


pupil_arousal_findings$arousal_from_pupil <- lmer(arousal ~ pup_basCor * Mean_gray_z  +(1+pup_basCor  | ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610& 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$arousal_from_pupil)
# what about valence

pupil_arousal_findings$pupil_from_valence <- lmer(pup_basCor ~ valence_c+Mean_gray_z  +(1 +Mean_gray_z | ssid) + 
                                                    (0 +valence_c | ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610& 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_arousal)
summary(pupil_arousal_findings$pupil_from_valence)


```


# Valence and arousal
```{r}

pupil_arousal_findings$pupil_from_ar_vale <- lmer(pup_basCor ~ (valence_c* arousal_c) + Mean_gray_z  +
                                                    (1 + Mean_gray_z | ssid),
                                                    # (0+ valence_c* arousal_c | ssid),
                                                  REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 & 
                                      arousal_outler == FALSE))

db_full4new_stim_screen_pupil_nopract$total_fix_dur_z<- scale(db_full4new_stim_screen_pupil_nopract$total_fix_dur, center = TRUE, scale = TRUE)[,1]

pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * total_fix_dur_z)+ Mean_gray_z  +
                                                        +
                                                    (1 + Mean_gray_z | ssid),
                                                    # (0+  total_fix_dur_z | ssid),
                                                  REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 & 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_ar_val_gaze )
summary(pupil_arousal_findings$pupil_from_ar_vale)


car::vif(pupil_arousal_findings$pupil_from_ar_val_gaze)

anova(pupil_arousal_findings$pupil_from_ar_vale, pupil_arousal_findings$pupil_from_arousal)
# interaction valence and arousal



```



control for gaze

```{r}
# avg fix dur
db_full4new_stim_screen_pupil_nopract$mean_fix_dur_z<- scale(db_full4new_stim_screen_pupil_nopract$mean_fix_dur, center = TRUE, scale = TRUE)[,1]

pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * mean_fix_dur_z)+ Mean_gray_z  +
                                                        +
                                                    (1 + Mean_gray_z | ssid),
                                                    # (0+  total_fix_dur_z | ssid),
                                                  REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 & 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_ar_val_gaze )
interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_val_gaze , pred = arousal_c, modx = mean_fix_dur_z)


# fix count


db_full4new_stim_screen_pupil_nopract$fix_count_z<- scale(db_full4new_stim_screen_pupil_nopract$fix_count, center = TRUE, scale = TRUE)[,1]

pupil_arousal_findings$pupil_from_ar_val_gaze <- lmer(pup_basCor ~ (valence_c * arousal_c * fix_count_z)+ Mean_gray_z  +
                                                        +
                                                    (1 + Mean_gray_z | ssid),
                                                    # (0+  total_fix_dur_z | ssid),
                                                  REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid_num< 500 & 
                                      arousal_outler == FALSE))

summary(pupil_arousal_findings$pupil_from_ar_val_gaze )

interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_val_gaze , pred = arousal_c, modx = mean_fix_dur_z)
```


```{r}
interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_vale , pred = valence_c, modx = arousal_c)

interactions::interact_plot(pupil_arousal_findings$pupil_from_ar_vale , pred =arousal_c , modx =  valence_c)

car::vif(pupil_arousal_findings$pupil_from_ar_vale) #curoff 2.5?

cor.test(db_full4new_stim_screen_pupil_nopract$valence_c, db_full4new_stim_screen_pupil_nopract$arousal_c)

rmcorr_valence_arousal <- rmcorr::rmcorr(participant = "ssid", measure1 = "valence_c", measure2 = "arousal_c", data = db_full4new_stim_screen_pupil_nopract)

plot(rmcorr_valence_arousal)

?rmcorr

db_full4new_stim_screen_pupil_nopract %>%
  group_by(stimIAPS)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ArousalMean, ValenceMean))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  theme_classic()


db_full4new_stim_screen_pupil_nopract %>%
  group_by(stimIAPS)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, valence_c))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  theme_classic()

```





```{r}
# with arousal as DV
pupil_arousal_findings$aroual_from_pup <- lmer(arousal ~ valence_c*pup_basCor *  BRIGHTNESSc  + (1| stimIAPS)+
                                                 (1 | ssid)+(0 +valence_c | ssid), REML = FALSE,
                      data = subset(db_full4new_stim_screen_pupil_nopract, pupil_outlier == FALSE & ssid!= 610& 
                                      arousal_outler == FALSE))
summary(pupil_arousal_findings$aroual_from_pup)

anova(pupil_arousal_findings$aroual_from_pup)


interactions::interact_plot(pupil_arousal_findings$aroual_from_pup , pred = valence_c, modx = pup_basCor)

interactions::interact_plot(pupil_arousal_findings$aroual_from_pup, pred =pup_basCor , modx =  valence_c)
# for positive valenced stimuli participants reporte higher arousal to more dilated pupils
#for negatively valence they report feeling negative even when they don't show strog pupil dilation
# same pattern
# pupil is more alighned with objective arousal in the positive trials

```

mixed model is consistent wth the previous plot, arousal effects on pupil is modulated by valence. in other words there is a consistent effect between pupil and self-repot of arousal more for positive emotions.


analyse the correlationla way
since we are aggregating we don't need to worry too much about brighteness as every person sees every stimuli in all brightness levels
CONCORDANCE

```{r}

db_full4new_stim_screen_pupil_nopract%>%
  group_by(mediansplit_self_valence_thissample, stimIAPS)%>%
  summarise_at(c("arousal", 'valence'), mean, na.rm = TRUE)%>%
  arrange(stimIAPS)



```

````{r}

test_anovas<- db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
    mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)


 db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
    mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
  theme_classic()+
  geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)+
   # ggpubr::stat_compare_means(paired = TRUE)+
  ggpubr::stat_compare_means()+
  facet_grid(~mediansplit_sample_arousal2)
  
  
  
  
  # let's identify the folks that show less of a difference between conditions.

flag_pos_neg<-  db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_valence2)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
    mutate(cor_pos_neg_diff = cortest[mediansplit_sample_valence2 == "More positive"] -
             cortest[mediansplit_sample_valence2 == "More negative"])%>%
    mutate(cor_pos_neg_diff_lab = if_else(cor_pos_neg_diff>0, "pos > neg", "neg > pos"))%>%
     group_by(ssid,cor_pos_neg_diff_lab)%>%
    summarise_at(c("cor_pos_neg_diff"), mean, na.rm = TRUE)
    
    
    # subset(cor_pos_neg_diff<= 0)%>%
        ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
  theme_classic()+
  geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)
   # ggpubr::stat_compare_means(paired = TRUE)+
  ggpubr::stat_compare_means()
  
```


```{r}
testcor <-cor(db_full4new_stim_screen_pupil_nopract$arousal, db_full4new_stim_screen_pupil_nopract$valence) 
  
testcor 
db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_valence2, mediansplit_sample_arousal2)%>%
    # mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
   mutate(cortest = cor(arousal_c, pup_basCor, method = 'kendall'))%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_sample_valence2, cortest, color = mediansplit_sample_valence2))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
  theme_classic()+
   facet_grid(~mediansplit_sample_arousal2)+
  geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)
   # ggpubr::stat_compare_means(paired = TRUE)+
  # ggpubr::stat_compare_means()
  
  

db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
    # mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
   mutate(cortest = cor(valence_c, pup_basCor, method = 'kendall'))%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_sample_arousal2, cortest, color = mediansplit_sample_arousal2))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
  theme_classic()+
   facet_grid(~mediansplit_sample_valence2)+
  geom_hline(yintercept = .2, linetype = 'dashed', alpha = .2)

```
  

# same as plot above but with valence in the correlation
```{r}
db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_sample_arousal2)%>%
    mutate(cortest = cor.test(valence_c, pup_basCor)$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_sample_arousal2, cortest, color = mediansplit_sample_arousal2))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
  theme_classic()
   # ggpubr::stat_compare_means(paired = TRUE)+
  ggpubr::stat_compare_means()
  
  
```
  
```{r}
# ground

 db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_ground_valence)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_ground_valence, cortest, color = mediansplit_ground_valence))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .3)+
        stat_summary( geom = 'pointrange')+
        stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
   # ggpubr::stat_compare_means(paired = TRUE)+
  # ggpubr::stat_compare_means()
   theme_classic()
```
 


```{r}
 # ground arousal with valence in the  corelation
 
 db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_ground_valence)%>%
    mutate(cortest = cor.test(valence_c, pup_basCor, use = "complete")$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_ground_valence, cortest, color = mediansplit_ground_valence))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .3)+
        stat_summary( geom = 'pointrange')+
        stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
   # ggpubr::stat_compare_means(paired = TRUE)+
  # ggpubr::stat_compare_means()
   theme_classic()




 db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_ground_valence, mediansplit_ground_arousal)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_ground_valence, cortest, color = mediansplit_ground_valence))+
           geom_bar(stat="summary", fun.y = "mean", alpha = .4)+
  
    geom_jitter(width = .1, alpha = .3)+
        stat_summary( geom = 'pointrange')+
        stat_summary(aes(group = ssid), geom = 'line', color = "black", alpha = .1)+
    ylab("R pupil vs self report arousal")+
   # ggpubr::stat_compare_means(paired = TRUE)+
  # ggpubr::stat_compare_means()
   theme_classic()+
   facet_grid(~mediansplit_ground_arousal)

```

 
```{r}
db_pupil_concordance_agg_ground <-  db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_ground_valence)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor, use = "complete")$estimate)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)

afex::aov_ez(id = "ssid",
             data = db_pupil_concordance_agg,
             between = NULL,
             within = 'mediansplit_ground_valence',
             dv = 'cortest')


unique(db_full4new_stim_screen_pupil_nopract$tNo)
db_pupil_concordance_agg_self <-  db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE)%>%
  subset( arousal_outler == FALSE & !is.na(Alexithymia))%>%
  subset(!is.na(Alexithymia))%>%
  # subset(Group == "NT")%>%
  subset(ssid_num<500)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_self_arousal, mediansplit_self_valence)%>%
    mutate(cortest = cor(arousal_c, pup_basCor, use = "complete"))%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)

afex::aov_ez(id = "ssid",
             data = test_anovas,
             between = NULL,
             within = 'mediansplit_sample_valence2',
             dv = 'cortest')


afex::aov_ez(id = "ssid",
             data = subset(test_anovas, mediansplit_sample_valence2 == "More positive"),
             between = NULL,
             within = 'mediansplit_sample_arousal2',
             dv = 'cortest')


afex::aov_ez(id = "ssid",
             data = db_pupil_concordance_agg_self,
             between = NULL,
             within = 'mediansplit_ground_valence',
             dv = 'cortest')

# valence and arousal


afex::aov_ez(id = "ssid",
             data = test_anovas,
             between = NULL,
             within = c('mediansplit_sample_arousal2','mediansplit_sample_valence2'),
             dv = 'cortest')





db_parsed_f_summ<- db_parsed_f %>%
  group_by(tNo, ssid)%>%
  summarise_at(c('Duration', 'mean_x', 'mean_y'), sum, na.rm = TRUE)
  ggplot(aes(tNo, Duration))+
  stat_summary(geom = 'pointrange')+
  geom_point(alpha = .1)


unique(db_parsed_f_summ$ssid)
unique(db_parsed_f_summ$tNo)
unique(db_full4new_stim_screen_pupil_nopract$tNo)
unique(db_full4new_stim_screen_pupil_nopract$ssid)
nrow(db_full4new_stim_screen_pupil_nopract)
nrow(db_parsed_f_summ)

table(is.na(db_parsed_f_summ$ssid))

db_parsed_f_summ$total_fix_dur<- db_parsed_f_summ$Duration


db_parsed_f_summ$tNo<- as.character(db_parsed_f_summ$tNo)
db_parsed_f_summ$ssid<- as.character(db_parsed_f_summ$ssid)
db_full4new_stim_screen_pupil_nopract$Duration<- NULL
db_full4new_stim_screen_pupil_nopract$total_fix_dur<- NULL

db_full4new_stim_screen_pupil_nopract<- left_join(db_full4new_stim_screen_pupil_nopract, db_parsed_f_summ)
  
```


I HAVE THE IMPRESSION THAT WHAT THIS ANALYSES GIVES US IS THE SAME AS THE MIXED MODEL AS THIS CONCIORDANCE CAN BE EXPRESSED IN TERMS FO THE SLOPE OF THE AOUSAL AND PUPIL RELATION


SO ION OTHER WORDS IS not that pupil tracksvalence is that arousal based pupil is modulated by valence

by why is that
- bias is responding? eg.. people response they feel negative even when they don't (ie. when we have no objective indicator that they do feel)
- is it something about the stimuli, positive arousal is sexual here?
- or arousal interferes with appraisal. high arousing stimuli are distracting??



timecourse models of pupil







Pupil tracks valence

```{r}

db_full4new_stim_screen_pupil_Anopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
  group_by(ssid)%>%
  mutate(pup_basCor_z = scale(pup_basCor, center = TRUE, scale = TRUE))%>%
    group_by(ssid, ground_valence_lab, Alexithymia)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(ground_valence_lab, pup_basCor_z,fill = ground_valence_lab))+
 geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
    # geom_ysidedensity(aes(x=stat(density),color = ground_valence_lab), alpha = .4)+
  p$graphstyle+
  xlab("valence")+
  ylab("Baseline corrected pupil")+
  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")


mediansplit_self_valence


db_full4new_stim_screen_pupil_nopract %>%
  # subset(pp_arousal_outler== FALSE)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
  subset(ssid_num<500)%>%
  group_by(ssid)%>%
  # mutate(pup_basCor_z = scale(pup_basCor, center = TRUE, scale = TRUE))%>%
    group_by(ssid, self_arousal_lab, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(self_arousal_lab, pup_basCor,fill = self_arousal_lab))+
 geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
    # geom_ysidedensity(aes(x=stat(density),color = ground_valence_lab), alpha = .4)+
  p$graphstyle+
  xlab("arousal")+
  ylab("Baseline corrected pupil")+
  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)





  db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler== FALSE)%>%
  subset(ssid< 500)%>%
  subset(pupil_outlier == FALSE)%>%
  subset(!is.na(Alexithymia))%>%
    group_by(ssid, self_arousal_lab, Alexithymia, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(self_arousal_lab, pup_basCor, color = self_arousal_lab,fill = self_arousal_lab))+
    geom_hline(yintercept = 0, linetype = "dashed", size = 2, alpha = .2)+
 # geom_half_violin(colour = FALSE, alpha = .3, size = 5)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .4, color = FALSE)+
      geom_jitter(width = .1, alpha=.2)+
    stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = 1, colour = "Black") +
  stat_summary(geom = 'pointrange', width =1)+
  p$graphstyle+
  xlab("Arousal")+
  ylab("Baseline corrected pupil")+

  scale_color_brewer(palette = "Dark2")+
   scale_fill_brewer(palette = "Dark2")+
  facet_grid(~mediansplit_self_valence)
  # facet_grid(~BRIGHTNESS_mediansplit)+
  ylim(-6,2.3)
  
  
  
  
  
  
table(is.na(db_full4new_stim_screen_pupil_nopract$mediansplit_self_valence))
```



combine timecourse


let's put pupil scr and ht prediction on subjective arousal



```{r}
rm(justone_test, lucy_plots, test)

# import the timecourse
unique(tmp.df4_full_stim_downs_jun2021)

tmp.df4_full_stim_downs_jun2021_with_beh2$

tmp.df4_full_stim_downs_jun2021 <- readRDS("~/OneDrive - Nexus365/InteroStudy2020/analysis/DataAnalysisJanuary2020/DataAnalysisJan2020/tmp.df4_full_stim_downs_jun2021.rds")


# merge with behavioural data
unique(db_full4new_stim_screen_pupil_nopract$ssid)
colnames(db_full4new_stim_screen_pupil_nopract)
colnames(tmp.df4_full_stim_downs_jun2021)

db_full4new_stim
colnames(db_full4new_stim_screen_pupil_nopract)
db_full4new_stim_subset<- db_full4new_stim_screen_pupil_nopract[,c(1:4,6:7,12:13,16,21:27, 31:56,80:81,84:96,98:114,116:124)]


# match types of merging variables
tmp.df4_full_stim_downs_jun2021_with_beh2$ssid<- as.character(tmp.df4_full_stim_downs_jun2021_with_beh2$ssid)

db_full4new_stim_subset$ssid<- as.character(db_full4new_stim_subset$ssid)

# tNo

tmp.df4_full_stim_downs_jun2021_with_beh2$tNo <- as.character(tmp.df4_full_stim_downs_jun2021_with_beh2$tNo)
db_full4new_stim_subset$tNo <- as.character(db_full4new_stim_subset$tNo)
unique(db_full4new_stim_subset$tNo)
unique(tmp.df4_full_stim_downs_jun2021_with_beh2$tNo)
# merge
nrow(tmp.df4_full_stim_downs_jun2021_with_beh2)
# 172168
# 87529

table(is.na(db_full4new_stim_subset$mediansplit_self_valence))

tmp.df4_full_stim_downs_jun2021_with_beh2$tNo<- as.numeric(tmp.df4_full_stim_downs_jun2021_with_beh2$tNo)
tmp.df4_full_stim_downs_jun2021_with_beh2<- tmp.df4_full_stim_downs_jun2021_with_beh2%>%
  arrange(ssid,tNo, timerezero3)


db_full4new_stim_subset$t
tmp.df4_full_stim_downs_jun2021_with_beh<- left_join(tmp.df4_full_stim_downs_jun2021_with_beh2, db_full4new_stim_subset)


unique(db_full4new_stim_subset$stimIAPS)

tmp.df4_full_stim_downs_jun2021_with_beh2$trialUnq

unique(tmp.df4_full_stim_downs_jun2021_with_beh2$ssid)
unique(db_full4new_stim_subset$ssid)


db_full4new_stim_subset$tNo<- as.numeric(db_full4new_stim_subset$tNo)


table(is.na(db_full4new_stim_subset$arousal))

tmp.df4_full_stim_downs_jun2021_with_behnew<- right_join(tmp.df4_full_stim_downs_jun2021_with_beh2, db_full4new_stim_subset,
                                                     by = c('ssid', 'tNo'))

nrow(tmp.df4_full_stim_downs_jun2021_with_beh)
# 172168 good

nrow(tmp.df4_full_stim_downs_jun2021_with_beh2)

nrow(tmp.df4_full_stim_downs_jun2021_with_behnew)
87529

table(is.na(tmp.df4_full_stim_downs_jun2021_with_behnew$mediansplit_self_valence))

subset(tmp.df4_full_stim_downs_jun2021_with_behnew, is.na(arousal))

unique(tmp.df4_full_stim_downs_jun2021_with_beh2$trialUnq)

```


```{r}
# check
tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(ground_arousal_lab))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= ground_arousal_lab, color = ground_arousal_lab), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=ground_arousal_lab, color = ground_arousal_lab), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   facet_grid(~mediansplit_self_valence)






```

pupil timecourse

```{r}

tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= mediansplit_sample_valence2, color = mediansplit_sample_valence2), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=mediansplit_sample_valence2, color = mediansplit_sample_valence2), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   facet_grid(~mediansplit_sample_arousal2)



tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= mediansplit_ground_arousal, color = mediansplit_ground_arousal), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=mediansplit_ground_arousal, color = mediansplit_ground_arousal), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   facet_grid(~mediansplit_ground_valence)

```


unique(tmp.df4_full_stim_downs_jun2021_with_beh$condition)

```{r}
tmp.df4_full_stim_downs_jun2021_with_beh$valencearousal_outliers<- if_else(tmp.df4_full_stim_downs_jun2021_with_beh$ArousalMean< 6.7 & 
                                                                    tmp.df4_full_stim_downs_jun2021_with_beh$mediansplit_ground_valence == "More negative", TRUE,
                                                                    if_else(tmp.df4_full_stim_downs_jun2021_with_beh$ArousalMean> 2.94 & 
                                                                    tmp.df4_full_stim_downs_jun2021_with_beh$mediansplit_ground_valence == "More positive", TRUE, FALSE))


tmp.df4_full_stim_downs_jun2021_with_beh %>%
  # subset(valencearousal_outliers == TRUE) %>%
  group_by(stimIAPS,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, ArousalMean))+
  # geom_boxplot()+
  # geom_point()+
  # geom_text(aes(label = stimIAPS))+
  stat_summary(geom = 'pointrange')+
  geom_hline(yintercept = 7.2)+
    geom_hline(yintercept = 6.7)+
   geom_hline(yintercept = 2.94)


  

# thismplot raises question regarding wthtehr the better correlation bwteen subjective and objective arousal for positive stomuli reflects the fact that for negative we don't have many low arousal ones. But this is unlikelly because the oposite us also true for posituive stimuli, notmany positive stomuli are rated as highly arousing in sekf report, so we would not expect


# less restruct varoability
tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  subset(valencearousal_outliers == TRUE) %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   facet_grid(~mediansplit_sample_valence2)
              
              
tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  subset(valencearousal_outliers == TRUE) %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=mediansplit_sample_arousal2, color = mediansplit_sample_arousal2), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   facet_grid(mediansplit_sample_valence2~.)+
  geom_hline(yintercept = 0)



```


unique(tmp.df4_full_stim_downs_jun2021_with_beh$timebin2)
# 30 time bins

```{r}
# pval<- cor.test(tmp.df4_full_stim_downs_jun2021_with_beh$sdevgaze_parsed, tmp.df4_full_stim_downs_jun2021_with_beh$pup_basCor)
# pval$estimate


tmp.df4_full_stim_downs_jun2021_with_beh$ssid_num<- as.numeric(as.character(tmp.df4_full_stim_downs_jun2021_with_beh$ssid))

library(purrr)

tmp.df4_full_stim_downs_jun2021_with_behnew<-tmp.df4_full_stim_downs_jun2021_with_behnew%>%
ungroup()%>%
  mutate(mediansplit_sample_valence2 = 
           if_else(ValenceMeanThisSample >= 
                     median(ValenceMeanThisSample,
                            na.rm = TRUE), 
                   "More positive", "More negative"))%>%
  mutate(mediansplit_sample_arousal2 = if_else(ArousalMeanThisSample >=
                                                  median(ArousalMeanThisSample,
                                                         na.rm = TRUE), "High", "Low"))



tmp.df4_full_stim_downs_jun2021_with_behnew$ssid_split_val_time<- paste0(tmp.df4_full_stim_downs_jun2021_with_behnew$ssid,          
paste0(tmp.df4_full_stim_downs_jun2021_with_behnew$mediansplit_sample_valence2,
       paste0(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)))


table(is.na(tmp.df4_full_stim_downs_jun2021_with_beh$mediansplit_self_valence))
unique(tmp.df4_full_stim_downs_jun2021_with_beh$ssid_split_val_time)
subset(tmp.df4_full_stim_downs_jun2021_with_beh, is.na(mediansplit_self_valence))

tmp.df4_full_stim_downs_jun2021_with_behnew$ssid_num<- as.numeric(tmp.df4_full_stim_downs_jun2021_with_behnew$ssid)

tmp.df4_full_stim_downs_jun2021_with_behnew <- tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  subset(ssid_num<500)%>%
  # subset(ssid == 320)%>%
  ungroup()%>%
  group_by(ssid_split_val_time)%>%
  mutate(cor_arous_pup = cor.test(pup_basCor, arousal_c, use = "complete")$estimate,
         cor_arous_pup_pval = cor.test(pup_basCor, arousal_c, use = "complete")$p.value)

tmp.df4_full_stim_downs_jun2021_with_behnew<- tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  group_by(timebin2)%>%
  mutate(mean_cor_pup = mean(cor_arous_pup, na.rm = TRUE), sd_cor_pup = sd(cor_arous_pup, na.rm = TRUE),
         mean_cor_pup_pva = mean(cor_arous_pup_pval, na.rm = TRUE), sd_cor_arous_pup_pval = sd(cor_arous_pup_pval, na.rm = TRUE))
  

tmp.df4_full_stim_downs_jun2021_with_behnew$pval_sign<- if_else(tmp.df4_full_stim_downs_jun2021_with_behnew$mean_cor_pup_pva < .05, tmp.df4_full_stim_downs_jun2021_with_behnew$mean_cor_pup_pva, NULL)



ttestcheck<- t.test(tmp.df4_full_stim_downs_jun2021_with_behnew$cor_arous_pup)
ttestcheck$p.value
ttestcheck$parameter

unique(tmp.df4_full_stim_downs_jun2021_with_behnew$ssid_split_val_time)

tmp.df4_full_stim_downs_jun2021_with_behnew$vale_timebin<- paste0(tmp.df4_full_stim_downs_jun2021_with_behnew$mediansplit_sample_valence2, paste0(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2))

# subset(tmp.df4_full_stim_downs_jun2021_with_behnew, vale_timebin == "More negative(2.6,2.82]")

tmp.df4_full_stim_downs_jun2021_with_behnew$ssid<- as.character(tmp.df4_full_stim_downs_jun2021_with_behnew$ssid)





tmp.df4_full_stim_downs_jun2021_with_behnew %>%
    group_by(ssid, mediansplit_sample_valence2, timebin2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  group_by(mediansplit_sample_valence2,timebin2)%>%
  mutate(pval = t.test(cor_arous_pup)$p.value, df = t.test(cor_arous_pup)$parameter)%>%
  group_by(mediansplit_sample_valence2,timebin2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(timerezero3, cor_arous_pup, color = mediansplit_sample_valence2))+
  # geom_s
  stat_summary(fun = mean, geom = 'line', size = 4)+
  # stat_summary(aes(y = pval), fun = mean, geom = 'line', linetype = "dashed", alpha = .2)+
  geom_hline(yintercept = .05, linetype = "dashed")+
  
  geom_hline(yintercept = .1, linetype = "dotted")+
  theme_classic()


nrow(left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, flag_pos_neg))
nrow(tmp.df4_full_stim_downs_jun2021_with_behnew)


tmp.df4_full_stim_downs_jun2021_with_behnew$cor_pos_neg_diff_lab<- NULL
tmp.df4_full_stim_downs_jun2021_with_behnew$cor_pos_neg_diff<-NULL
tmp.df4_full_stim_downs_jun2021_with_behnew<- left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, flag_pos_neg)

tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  subset(!is.na(cor_pos_neg_diff_lab))%>%
  # subset(cor_pos_neg_diff_lab == "pos > neg") %>%
  group_by(ssid, mediansplit_sample_valence2, timebin2,cor_pos_neg_diff_lab) %>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  group_by(mediansplit_sample_valence2,timebin2) %>%
  mutate(pval = t.test(cor_arous_pup)$p.value, df = t.test(cor_arous_pup)$parameter)%>%
  group_by(mediansplit_sample_valence2,timebin2,cor_pos_neg_diff_lab)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE) %>%
  # ggplot(aes(timerezero3, cor_arous_pup, color = mediansplit_sample_valence2))+
  ggplot(aes(timerezero3, cor_arous_pup, color =  cor_pos_neg_diff_lab))+
  # geom_s
  stat_summary(fun = mean, geom = 'line', size = 4)+
  # stat_summary(aes(y = pval), fun = mean, geom = 'line', linetype = "dashed", alpha = .2)+
  geom_hline(yintercept = .05, linetype = "dashed")+
  
  geom_hline(yintercept = .1, linetype = "dotted")+
  geom_vline(xintercept = 1, linetype = "dotted")+
  # geom_rect(xmin = .5, xmax = 1, ymin = -.2, ymax = 1, alpha = .01, color = FALSE)+
  theme_classic()+
    facet_grid(~mediansplit_sample_valence2)



range(tmp.df4_full_stim_downs_jun2021_with_behnew$pval_sign, na.rm = TRUE)
tmp.df4_full_stim_downs_jun2021_with_behnew$ssid_split_val_time





# let's identify the people whio show the negative arousal effect

```

timecourse analyss using lmer

1- select a bin
2 - fit a lmer
3 - save statistics (estimate and p value)
3 - create clusters (p value < .05 in at least 3 consecutive bins)

```{r}

tmp.df4_full_stim_downs_jun2021_with_behnew$
i = 1

nbins<- unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)

tmp.df4_full_stim_downs_jun2021_with_behnew

unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)
lmer_bin_pup<- list() 

# <- nrow(nbins)


nbins<- unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin2)


tmp.df4_full_stim_downs_jun2021_with_behnew$Label<- substr(tmp.df4_full_stim_downs_jun2021_with_behnew$stimIAPS, 1,8)

imageJ_IAPS$Label
nrow(tmp.df4_full_stim_downs_jun2021_with_behnew)
# 62921
nrow(left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, imageJ_IAPS))


tmp.df4_full_stim_downs_jun2021_with_behnew<- left_join(tmp.df4_full_stim_downs_jun2021_with_behnew, imageJ_IAPS)


tmp.df4_full_stim_downs_jun2021_with_behnew$Mean_gray_z<- scale(tmp.df4_full_stim_downs_jun2021_with_behnew$Mean, scale = TRUE, center = TRUE)[,1]


test<- lmer(pup_basCor ~ arousal_c+Mean_gray_z  +(1 +Mean_gray_z | ssid), 
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    timebin2 == nbins[b]))

summary(test)

tmp.df4_full_stim_downs_jun2021_with_behnew$tNo
"(0.0411,0.261]"
subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    timebin2 == nbins[28] & ssid == 341 & tNo == 3)

test
rm(i)

i = 1 # remember to always rezero it
# b = 34
nbins<- nbins[1:28]
unique(nbins)

# nbins<- as

# positive

# create number s for time bins
tmp.df4_full_stim_downs_jun2021_with_behnew<- tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  group_by(ssid, stimIAPS)%>%
  mutate(timebin_no = 1:n())

tmp.df4_full_stim_downs_jun2021_with_behnew%>%
  # group_by(ssid, stimIAPS)
  ggplot(aes(timebin_no))+
  geom_histogram()+
  geom_vline(xintercept = 28)+
  geom_hline(yintercept = 200)

```


```{r}


nrow(nbins)
nbins = 30
timecourse_result_df <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))

View(timecourse_result_df)

nbins<- nbins[1:28]



i = 1 # remember to always rezero it
# b = 34

nbins<- unique(tmp.df4_full_stim_downs_jun2021_with_behnew$timebin_no)

unique(nbins)
for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ arousal_c+Mean_gray_z  +(1 +Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_self_valence == "More positive" & 
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}


# timecourse_result_df_pos<- timecourse_result_df

timecourse_result_df%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


```


# negative lmer
```{r}

timecourse_result_df_neg <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1

for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ arousal_c+Mean_gray_z  +(1 +Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_ground_valence == "More negative" & 
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_neg[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_neg[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_neg[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_neg[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_neg[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}


View(timecourse_result_df_neg)

# whether I use ground valence, self valemce or this sample average valence, I get the same patterns

timecourse_result_df_neg%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .1, color = 'red', size = 1.5)

```



```{r}
timecourse_result_df$Valence <- "More negative"

timecourse_result_df_neg$Valence<- "More positive"

bind_rows(timecourse_result_df,timecourse_result_df_neg)%>%
    ggplot(aes(timebins, t, color = Valence))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


```
# use valence to pupil
```{r}
timecourse_result_df_val <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1

for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ arousal_c+ valence_c+ Mean_gray_z  +(1 + Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    # mediansplit_self_valence == "More negative" & 
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_val[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_val[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_val[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_val[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_val[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}

# valence to pupil
timecourse_result_df_val%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)

```


# use valence to pupil

```{r}
timecourse_result_df_val_and_ar <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1

for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ valence_c+ arousal+Mean_gray_z  +(1 + Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    # mediansplit_self_valence == "More negative" & 
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_val_and_ar[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_val_and_ar[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_val_and_ar[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_val_and_ar[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_val_and_ar[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}


timecourse_result_df_val_and_ar%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)



```

valence for high vs low
```{r}
timecourse_result_df_val_and_ar_neg <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1

for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ valence_c + Mean_gray_z  +(1 + Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_sample_arousal2 == "High" &
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_val_and_ar_neg[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_val_and_ar_neg[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_val_and_ar_neg[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_val_and_ar_neg[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_val_and_ar_neg[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}

View(timecourse_result_df_val_and_ar_neg)

timecourse_result_df_val_and_ar_neg%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)+
  ggtitle("pupil ~ valence for high arousal")

```

# 

```{r}
timecourse_result_df_val_and_ar_low <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1

for (b in 1:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_basCor ~ valence_c +Mean_gray_z  +(1 + Mean_gray_z | ssid) +
                                (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_sample_arousal2 == "Low" &
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_val_and_ar_low[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_val_and_ar_low[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_val_and_ar_low[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_val_and_ar_low[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_val_and_ar_low[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}

View(timecourse_result_df_val_and_ar_neg)

timecourse_result_df_val_and_ar_low%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


```




velocity analyses
```{r}
# thought maybe it has soemthing to to with the velcity, velocity is sidderent

install.packages("pspline")
library(pspline)

predict(sm.spline(tmp.df4_full_stim_downs_jun2021_with_beh$timerezero3, 
                  tmp.df4_full_stim_downs_jun2021_with_beh$pup_basCor), 
        tmp.df4_full_stim_downs_jun2021_with_beh$timerezero3, 1)

D(tmp.df4_full_stim_downs_jun2021_with_beh$pup_basCor, 'x')

tmp.df4_full_stim_downs_jun2021_with_behnew

tmp.df4_full_stim_downs_jun2021_with_behnew <- tmp.df4_full_stim_downs_jun2021_with_behnew %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  # subset(valencearousal_outliers == TRUE) %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  group_by(ssid, stimIAPS) %>%
  mutate(pup_velocity = (abs(lag(pup_basCor)-pup_basCor)/abs(lag(timerezero3)-timerezero3)))
  # mutate(pup_velocity_peak = max(pup_velocity, na.rm = TRUE))


tmp.df4_full_stim_downs_jun2021_with_behnew$pup_velocity

# global
timecourse_result_df_vel <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1
b

tmp.df4_full_stim_downs_jun2021_with_behnew$pup_velocity

lmer_bin_pup  <- lmer(pup_velocity ~ arousal_c + Mean_gray_z  + (1 + Mean_gray_z | ssid),
                      REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    timebin_no == nbins[2]))

for (b in 2:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_velocity ~ arousal_c + Mean_gray_z  + (1 + Mean_gray_z | ssid),
                                # (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    # !is.na== "More negative" &
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_vel[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_vel[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_vel[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_vel[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_vel[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}


View(timecourse_result_df_vel)



# whether I use ground valence, self valemce or this sample average valence, I get the same patterns

timecourse_result_df_vel%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


# arousal rating is related to velocity around the recovery time 1:2is, and later around 4


```


Velocity by valence
```{r}


timecourse_result_df_vel_pos <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1
# b

tmp.df4_full_stim_downs_jun2021_with_behnew$pup_velocity


for (b in 2:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_velocity ~ arousal_c + Mean_gray_z  + (1  | ssid),
                                # (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_sample_valence2== "More positive" &
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_vel_pos[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_vel_pos[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_vel_pos[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_vel_pos[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_vel_pos[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}

# 11 singularity cases

View(timecourse_result_df_vel_pos)




# whether I use ground valence, self valemce or this sample average valence, I get the same patterns

timecourse_result_df_vel_pos%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


# arousal rating is related to velocity around the recovery time 1 to 1.6



```



Negative velocity

```{r}

timecourse_result_df_vel_neg <- data.frame(timebins= rep(NA, 30), Estimate= rep(NA, 30), 
                                   t=rep(NA, 30), p=rep(NA, 30), cov= as.character(rep(NA,30)))


i = 1
b

tmp.df4_full_stim_downs_jun2021_with_behnew$pup_velocity


for (b in 2:length(nbins)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer %i", nbins[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lmer_bin_pup  <- lmer(pup_velocity ~ arousal_c + Mean_gray_z  + (1 | ssid),
                                # (1|stimIAPS),
                              REML = FALSE,
                      data = subset(tmp.df4_full_stim_downs_jun2021_with_behnew,
                                    mediansplit_sample_valence2== "More negative" &
                                    timebin_no == nbins[b]))

        #store results from the simulation
            lmer_bin_pup_summary<- summary(lmer_bin_pup)
            timecourse_result_df_vel_neg[i,1]<- tmp.df4_full_stim_downs_jun2021_with_behnew$timerezero3[b] #save the exact value of time bin
            timecourse_result_df_vel_neg[i,2]<- lmer_bin_pup_summary[["coefficients"]][2,1] # s
            timecourse_result_df_vel_neg[i,3]<-lmer_bin_pup_summary[["coefficients"]][2,4] # t statistic
            timecourse_result_df_vel_neg[i,4]<-lmer_bin_pup_summary[["coefficients"]][2,5] # p value
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            timecourse_result_df_vel_neg[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
                                                   != 0,
                                               lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}

# 11 singularity cases

View(timecourse_result_df_vel_neg)




# whether I use ground valence, self valemce or this sample average valence, I get the same patterns

timecourse_result_df_vel_neg%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .05, color = 'red', size = 1.5)


# arousal rating is related to velocity around the recovery time 1 to 1.6


```

A few plots for velocity
```{r}
  ggplot(aes(x = timerezero3, y = pup_velocity))+
  stat_smooth(aes(group= mediansplit_sample_valence2, color = mediansplit_sample_valence2), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=mediansplit_sample_valence2, color = mediansplit_sample_valence2), size = 3)+  
theme_classic()+
  ylab("Pupil velocity")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
   # facet_grid(~mediansplit_sample_valence2)+
     geom_vline(xintercept = 1)



tmp.df4_full_stim_downs_jun2021_with_beh$timerezero3
tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  subset(timerezero3>1 ) %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  group_by(ssid, stimIAPS)%>%
  mutate(pup_velocity = (abs(lag(pup_basCor)-pup_basCor))/(abs(lag(timerezero3)-timerezero3)))%>%
  mutate(pup_velocity_peak = max(pup_velocity, na.rm = TRUE))%>%
  group_by(ssid,mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(x = mediansplit_sample_valence2, y = pup_velocity))+
  # geom_jitter(width = .1, alpha = .1)+
  stat_summary(geom = 'pointrange')+
  theme_classic()+
  ggpubr::stat_compare_means()+
  ylab("Pupil velocity (z)")+
  xlab("Time (s)")+
  facet_grid(~mediansplit_sample_arousal2)


tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(mediansplit_sample_arousal2))%>%
  subset(timerezero3>1) %>%
  # subset(!is.na(mediansplit_sample_arousal2))%>%
  group_by(ssid, stimIAPS)%>%
  mutate(pup_velocity = (abs(lag(pup_basCor)-pup_basCor))/(abs(lag(timerezero3)-timerezero3)))%>%
  mutate(pup_velocity_peak = max(pup_velocity, na.rm = TRUE))%>%
  group_by(ssid,mediansplit_sample_valence2,mediansplit_sample_arousal2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(x = mediansplit_sample_valence2, y = pup_velocity_peak))+
  geom_jitter(width = .1, alpha = .1)+
  stat_summary(geom = 'pointrange')+
  theme_classic()+
  ggpubr::stat_compare_means()+
  ylab("peak Pupil velocity (z)")+
  xlab("Time (s)")+
  facet_grid(~mediansplit_sample_arousal2)
  
  

```
```{r}
tmp.df4_full_stim_downs_jun2021_with_behnew%>%
    group_by(mediansplit_self_valence, timebin2)%>%
summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(timerezero3, cor_arous_pup, color = mediansplit_self_valence))+
  # geom_s
  stat_summary(fun = mean, geom = 'pointrange')+
  stat_summary(aes(y = cor_arous_pup_pval), fun = mean, geom = 'line', linetype = "dashed")+
  geom_hline(yintercept = .1, linetype = "dashed")
  geom_rect(aes(xmin = timerezero3, xmax = dplyr::lead(timerezero3), ymin =.63 , ymax =.66 , fill = pval_sign), 
            alpha = 0.8)
  
  
  
  # stat_smooth(aes(group = ssid), se = F, alpha = .1)
  # mutate(
  #   model = map(data, ~lm(pup_basCor ~ arousal_c, data = .)),
  #   cor = map(data, possibly(
  #     ~tidy(cor.test(.x$pup_basCor, .x$arousal_c), 3), otherwise = data.frame())
  #   )
  # )



```


# self arousal

```{r}

tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(ground_arousal_lab))%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= ground_arousal_lab, color = ground_arousal_lab), fun = mean,geom = "line",
               se = F, alpha = .1, size = 1.5)+
  stat_smooth(aes(group=ground_arousal_lab, color = ground_arousal_lab), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)+
  facet_grid(~mediansplit_self_valence)



```


```{r}

# plot with valence
tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(ground_arousal_lab))%>%
  group_by(ssid,Alexithymia, mediansplit_ground_valence, timebin2)%>%
  ggplot(aes(x = timerezero3, y = pup_basCor))+
  stat_smooth(aes(group= mediansplit_ground_valence, color = mediansplit_ground_valence), fun = mean,geom = "line",
               se = F, size = 1.5)+
  # stat_smooth(aes(group=ground_valence_lab, color = ground_valence_lab), size = 3)+  
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  xlim(0,6)



test<- cor.test(db_2021_pupil_summ$hr_interp,db_2021_pupil_summ$pup_basCor)

test$p.value
tmp.df4_full_stim_downs_jun2021_with_beh$ssid_ground_valence<- paste0(tmp.df4_full_stim_downs_jun2021_with_beh$ssid, paste0(tmp.df4_full_stim_downs_jun2021_with_beh$ground_arousal_lab))

tmp.df4_full_stim_downs_jun2021_with_beh %>%
  subset(!is.na(self_arousal_lab))%>%
  
  
  db_full4new_stim_screen_pupil_nopract
  
  
  group_by(ssid,Alexithymia, ground_valence_lab, timebin2)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
  mutate(pcortest = cor.test(arousal_c, pup_basCor)$p.value)%>%
  group_by(ssid,Alexithymia, self_arousal_lab, timebin2)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE) %>%
  ggplot(aes(x = timerezero3, y = cortest))+

  stat_smooth(aes(group= self_arousal_lab, color = self_arousal_lab), fun = mean,geom = "line",
               se = F, size = 1.5)+
theme_classic()+
  ylab("Pupil size (z)")+
  xlab("Time (s)")+
  # p$graphstyle+
  scale_color_brewer(palette = "Dark2")+
  geom_hline(yintercept = .05, linetype = "dashed")+
  xlim(0,6)


# does arousing stimuli impairs appraisal


 tmp.df4_full_stim_downs_jun2021_with_beh %>%
   ggplot(aes(ground_arousal_lab, BRIGHTNESS))+
   geom_boxplot()+
   geom_point()+
   # stat_summary(geom = 'boxplot')+
   facet_grid(~mediansplit_self_valence)
```



```{r}
 db_full4new_stim_screen_pupil_nopract %>%
  subset(pupil_outlier == FALSE & arousal_outler == FALSE & !is.na(Alexithymia) & ssid!= 610)%>%
    # group_by(ssid,Alexithymia, is_valence_high)%>%
   group_by(ssid,Alexithymia, mediansplit_self_valence)%>%
    mutate(cortest = cor.test(arousal_c, pup_basCor)$estimate)%>%
   group_by(ssid,Alexithymia, mediansplit_self_valence, self_arousal_lab)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
        ggplot(aes(mediansplit_self_valence, cortest, color = mediansplit_self_valence))+
           geom_bar(stat="summary", fun.y = "mean")+
  
    geom_jitter(width = .1, alpha = .1)+
        stat_summary( geom = 'pointrange')+
          # stat_summary(aes(group = ssid), geom = 'line')+
    ylab("R pupil vs self report arousal")+
   # ggpubr::stat_compare_means(paired = TRUE)+
  # ggpubr::stat_compare_means(comparisons = my_comparisons, paired = TRUE)
      facet_grid(~self_arousal_lab)

```



SCR

```{r}

unique(db_full4new_stim_screen_pupil_nopract$ssid)
View(flag_pos_neg)
db_full4new_stim_screen_pupil_nopract$mediansplit_self_arousal <- if_else(db_full4new_stim_screen_pupil_nopract$arousal> median(db_full4new_stim_screen_pupil_nopract$arousal, na.rm = TRUE), "High", "Low")



db_full4new_stim_screen_pupil_nopract$mediansplit_self_arousal_thissample <- if_else(db_full4new_stim_screen_pupil_nopract$arousal> median(db_full4new_stim_screen_pupil_nopract$ArousalMeanThisSample, na.rm = TRUE), "High", "Low")

unique(db_full4new_stim_screen_pupil_nopract2$ssid)

db_full4new_stim_screen_pupil_nopract_nt<- subset(db_full4new_stim_screen_pupil_nopract, ssid_num<500)

nrow(db_full4new_stim_screen_pupil_nopract_nt)
# 2233
# nrow(left_join(db_full4new_stim_screen_pupil_nopract_nt, flag_pos_neg))

db_full4new_stim_screen_pupil_nopract_nt<- left_join(db_full4new_stim_screen_pupil_nopract_nt, flag_pos_neg)



unique(db_full4new_stim_screen_pupil_nopract_nt$stimDescription)
db_full4new_stim_screen_pupil_nopract_nt %>%
  subset(ssid_num< 500)%>%
    subset(pp_arousal_outler == FALSE)%>%
  subset( arousal_outler == FALSE )%>%
       subset(!is.na(Alexithymia))%>%
   group_by(ssid)%>%
  mutate(BIO_CDA.SCR_z = scale((BIO_CDA.SCR))[,1])%>%
  ungroup()%>%
    group_by(ssid,cor_pos_neg_diff_lab, mediansplit_self_valence)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_self_valence, BIO_CDA.SCR_z))+
      stat_summary(geom = 'pointrange')+
  
  
     # geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  geom_jitter(width = .2, alpha = .2)+
  stat_summary(aes(group = ssid), geom = 'line', color = "gray", alpha = .1)+
  # geom_boxplot(alpha = .1)+
  # facet_grid(~cor_pos_neg_diff_lab)+
  ggpubr::stat_compare_means()

BIO_CDA.ISCR_z

db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid)%>%
  mutate(BIO_TTP.AmpSum_z = scale(BIO_TTP.AmpSum, center = TRUE, scale = TRUE)[,1])%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, BIO_TTP.AmpSum_z))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()


db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid,mediansplit_sample_valence2)%>%
  # mutate(BIO_TTP.AmpSum_z = scale(BIO_TTP.AmpSum, center = TRUE, scale = TRUE)[,1])%>%
  # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal, BIO_CDA.ISCR_z))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  facet_grid(~mediansplit_sample_valence2)

db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid,mediansplit_sample_valence2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, BIO_CDA.PhasicMax_z, color =mediansplit_sample_valence2))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()




db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid,ground_arousal_lab)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_c, BIO_CDA.PhasicMax_z, color =ground_arousal_lab))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  
  
  
  


```

# scr model

<!-- # average aroual and rating over all trials don't track well the expected arousal relation -->
```{r}

unique(db_full4new_stim_screen_pupil_nopract_nt$condition)
pupil_arousal_findings$arousal_scr <- lmer(log(BIO_CDA.SCR+.1) ~ arousal_c*TASc +(1|ssid) + (1|stimIAPS),
                        REML = FALSE,
                        data = subset(db_full4new_stim_screen_pupil_nopract_nt,
                                      pp_arousal_outler == FALSE &
                                      mediansplit_self_valence == "More positive"))

summary(pupil_arousal_findings$arousal_scr)
anova(pupil_arousal_findings$arousal_phas_max)


BIO_CDA.PhasicMax


pupil_arousal_findings$arousal_scr <- lmer(BIO_CDA.PhasicMax_z ~ valence_c +(1+arousal_c|ssid),
                        REML = FALSE,
                        data = subset(db_full4new_stim_screen_pupil_nopract_nt,
                                      pp_arousal_outler == FALSE))
                                      # mediansplit_self_valence == "More positive"))

summary(pupil_arousal_findings$arousal_scr)
anova(pupil_arousal_findings$arousal_phas_max)


db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid, cor_pos_neg_diff_lab)%>%
  ggplot(aes(cor_pos_neg_diff_lab, TAS))+
  geom_point()+
  stat_summary(geom = 'pointrange')
```

summary(pupil_arousal_findings$arousal_scr)
anova(pupil_arousal_findings$arousal_phas_max)


test<- subset(db_full4new_stim_screen_pupil_nopract, pp_arousal_outler == FALSE & ssid == 310)
test$Alexithymia
table(test$mediansplit_self_valence)

# try the anova grouping
```{r}

afex::aov_ez(data = (db_full4new_stim_screen_pupil_nopract %>%
    subset(pp_arousal_outler == FALSE)%>%
      # subset(is_scr_nonresponder == FALSE )%>%
      subset(!is.na(Alexithymia))%>%
      group_by(ssid, mediansplit_ground_valence)%>%
      # group_by(ssid, Alexithymia,mediansplit_self_arousal, mediansplit_self_valence)%>%
      summarise_if(is.numeric, mean, na.rm = TRUE)),
    id = "ssid",
    between = NULL,
    within = c("mediansplit_ground_valence"),
    dv = "BIO_CDA.SCR_z")




colnames(db_full4new_stim_screen_pupil_nopract)


ggcorrplot::ggcorrplot(cor(db_full4new_stim_screen_pupil_nopract[,c(19,115,125:128,27,26)], use = "complete"))

```


    
BIO_CDA.SCR_z

# indovidual plots
```{r}

facet_hist_scr<- db_full4new_stim_screen_pupil_nopract %>%
  subset(ssid > 303)%>%
  subset(arousal_outler == FALSE )%>%
       subset(!is.na(Alexithymia))%>%
  ggplot(aes(log1p(BIO_CDA.SCR+1)))+
  geom_histogram(bins = )+
  facet_wrap(~ssid)

```



# what's up with non responders

# 312
# 318 only one rspinse?
# 320
# 321
# 327
# 329
# 332
# 335 (only one response)
# 342
# 343
# 600
# 603
# 606 (only one response)
# 608
# 900
# 56- 15

# that would leave us with 41 people

```{r}
unique(db_full4new_stim_screen_pupil_nopract$ssid)

range(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)

mean(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)
sd(db_full4new_stim_screen_pupil_nopract$BIO_CDA.SCR, na.rm = TRUE)

db_full4new_stim_screen_pupil_nopract%>%
  ggplot(aes(BIO_CDA.SCR))+
  geom_histogram(bins = 500)
  xlim(0,.01)
  
  
  
db_full4new_stim_screen_pupil_nopract%>%
  subset(ssid == 312)%>%
  ggplot(aes(BIO_CDA.AmpSum))+
  geom_histogram(bins = 500)+
  xlim(0,.05)

```


```{r}
db_full4new_stim_screen_pupil_nopract$is_scr_nonresponder<- if_else(db_full4new_stim_screen_pupil_nopract$BIO_CDA.AmpSum< .1, TRUE, FALSE)

 db_full4new_stim_screen_pupil_nopract %>%
  subset(ssid > 303)%>%
  subset(arousal_outler == FALSE )%>%
       subset(!is.na(Alexithymia))%>%
  ggplot(aes(log1p(BIO_CDA.SCR+1)))+
  geom_histogram(bins = )+
   facet_grid(~is_scr_nonresponder)
  facet_wrap(~ssid)
  
  
  
  db_full4new_stim_screen_pupil_nopract%>%
    group_by(ssid,is_scr_nonresponder)%>%
    mutate(scr_nonresp_prop = n())%>%
      group_by(ssid)%>%
     mutate(scr_nonresp_prop = scr_nonresp_prop/n())%>%
    ggplot(aes(as.factor(ssid), scr_nonresp_prop))+
    geom_bar(stat="summary", fun.y = "mean", alpha = .2, width = .5)
  
  
# test<-  subset(db_full4new_stim_screen_pupil_nopract, ssid == 321)
    

  
  table(db_full4new_stim_screen_pupil_nopract_nt$is_scr_nonresponder)
  db_full4new_stim_screen_pupil_nopract_nt %>%
    subset(is_scr_nonresponder != FALSE)%>%
    subset(pp_arousal_outler == FALSE)%>%
    subset(!ssid %in% c("304","321", "323", "318","339","331","325"))%>%
  # subset( arousal_outler == FALSE )%>%
       subset(!is.na(Alexithymia))%>%
   group_by(ssid)%>%
  mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
  # ungroup()%>%
    group_by(ssid, Alexithymia,mediansplit_sample_arousal2, mediansplit_sample_valence2)%>%
    summarise_if(is.numeric, mean, na.rm = TRUE)%>%
    # group_by(ssid)%>%
    # mutate(SCR_trend = BIO_CDA.SCR_z[mediansplit_ground_arousal == "Higher arousal"] -
             # BIO_CDA.SCR_z[mediansplit_ground_arousal == "Lower arousal"])
    # mutate(trendscr = if_else(SCR_trend> 0, "high to high", "high to low"))
  
    # group_by(ssid, Alexithymia,mediansplit_ground_arousal,trendscr)%>%
    # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_sample_arousal2, BIO_CDA.SCR_z))+
      stat_summary(geom = 'pointrange')+
     stat_summary(aes(group = ssid), geom = 'line')+
  
     # geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  geom_jitter(width = .2, alpha = .2)
    facet_grid(~mediansplit_sample_valence2)
    # geom_boxplot(alpha = .1)
    # geom_text(aes(label = ssid))
    facet_grid(~trendscr)
    # ylim(-1,1)
  
  
304, 318, 339, 331,325



scr_models<- list()

db_full4new_stim_screen_pupil_nopract_nt %>%
    subset(is_scr_nonresponder == FALSE)%>%
    subset(pp_arousal_outler == FALSE)%>%
    subset(!ssid %in% c("304","321", "323", "318","339","331","325"))
  # subset( arousal_outler == FALSE )%>%


db_full4new_stim_screen_pupil_nopract_nt_scr_excluding_atypical<- db_full4new_stim_screen_pupil_nopract_nt%>%
                        subset(!ssid %in% c("304","321", "323", "318","339","331","325"))
  # subset(is_scr_nonresponder == FALSE)%>%
  group_by(ssid)%>%
  mutate(BIO_CDA.PhasicMax_z = scale(BIO_CDA.PhasicMax, center = TRUE, scale = TRUE)[,1])%>%
 mutate(valence_z = scale(valence, center = TRUE, scale = TRUE)[,1])%>%
    mutate(arousal_z = scale(arousal, center = TRUE, scale = TRUE)[,1])


scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.PhasicMax+.1) ~ valence_z + arousal_z+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data = db_full4new_stim_screen_pupil_nopract_nt_scr_excluding_atypical)


scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.PhasicMax+.1) ~ valence_c + arousal_c+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  db_full4new_stim_screen_pupil_nopract_nt)
                        # subset(!ssid %in% c("304","321", "323", "318","339","331","325"))%>%
  subset(is_scr_nonresponder != FALSE))
                          
summary(scr_models$scr_excluding_atypical)                     
                          
(db_full4new_stim_screen_pupil_nopract_nt%>%
                        subset(!ssid %in% c("304","321", "323", "318","339","331","325"))))

summary(scr_models$scr_excluding_atypical)
```
<!-- one of the worries I had was that maybe there was a bias such that valence was confounded with arousal, but if this was true, then positive stimuli woul;d be rated as more arousing -->
  
  
    db_full4new_stim_screen_pupil_nopract %>%
    subset(is_scr_nonresponder == FALSE)%>%
    subset(pp_arousal_outler == FALSE)%>%
  subset( arousal_outler == FALSE )%>%
       subset(!is.na(Alexithymia))%>%
   group_by(ssid)%>%
  mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
      mutate(arousal_z = scale(arousal +.1))%>%
  ungroup()%>%
    # group_by(ssid, Alexithymia,mediansplit_self_arousal)%>%
    # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal_z, BIO_CDA.SCR_z))+
      geom_point()+
      # geom_smooth(aes(group = ssid),method = 'lm', se = F)+
      geom_smooth(method = 'lm', se = F)+
      facet_grid(~ground_arousal_lab)
  
     # geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  geom_jitter(width = .2, alpha = .2)+
    ylim(-1,1)
  
  # panel for many folks
  db_full4new_stim_screen_pupil_nopract %>%
  # subset(ssid == 314)%>%
    group_by(ssid)%>%
    mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
    mutate(arousal_z = scale(arousal +.1))%>%
  ggplot(aes(arousal_z,BIO_CDA.SCR_z, color =mediansplit_ground_valence , shape =mediansplit_ground_arousal))+
    geom_point()+
    geom_smooth(method = 'lm', se = F)+
    facet_wrap(~ssid)+
    ggpubr::stat_cor()
  
    db_full4new_stim_screen_pupil_nopract %>%
  # subset(ssid == 314)%>%
    group_by(ssid)%>%
    mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
    mutate(arousal_z = scale(arousal +.1))%>%
  ggplot(aes(pup_basCor,BIO_CDA.SCR_z))+
    geom_point()+
    geom_smooth(method = 'lm', se = F)+
    facet_wrap(~ssid)+
    ggpubr::stat_cor()
    
    
        db_full %>%
  # subset(ssid == 314)%>%
    group_by(ssid)%>%
    mutate(BIO_Mean_HR = scale(BIO_Mean_HR, scale = FALSE)[,1])%>%
    mutate(arousal_z = scale(arousal +.1))%>%
  ggplot(aes(BIO_Mean_HR,log1p(BIO_CDA.PhasicMax+.1)))+
    geom_point()+
    geom_smooth(aes(group = ssid),method = 'lm', se = F)
    facet_wrap(~ssid)+
    ggpubr::stat_cor()
    
    
    
    


Let's try some 3 d visualisations
continue here


````{r}


db_full4new_stim_screen_pupil_nopract_nt %>%
  group_by(ssid, mediansplit_ground_arousal, mediansplit_ground_valence,scr_utl)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  subset(scr_utl == "notoutlier")%>%
  ggplot(aes(mediansplit_ground_arousal , BIO_CDA.PhasicMax_z))+
    geom_jitter(alpha = .1, width = .1)+
  stat_summary(geom = 'pointrange')+
  stat_summary(aes(group = ssid), geom = "line", alpha = .1)+
  facet_grid(~mediansplit_ground_valence)


db_full4new_stim_screen_pupil_nopract_nt%>%
  ggplot(aes(BIO_CDA.PhasicMax_z))+
  geom_histogram()


db_full4new_stim_screen_pupil_nopract_nt<- db_full4new_stim_screen_pupil_nopract_nt%>%
  mutate(scr_utl = if_else(BIO_CDA.PhasicMax_z > 5, "outlier", "notoutlier"))
  
```

  # + arousal_c
  
```{r}
scr_models$scr_excluding_atypical<- lmer(log(BIO_CDA.AmpSum_collapsed+.1) ~  arousal_c*valence_c * TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))


summary(scr_models$scr_excluding_atypical)
plot(scr_models$scr_excluding_atypical)

interactions::interact_plot(scr_models$scr_excluding_atypical, pred = arousal_c, modx = valence_c, mod2 = TASc)


db_full4new_stim_screen_pupil_nopract_nt%>%
  ggplot(aes())


scr_models$scr1<- lmer(log(BIO_CDA.PhasicMax_collapsed+.1) ~  arousal_c*valence_c * TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))

summary(scr_models$scr1)
scr_models$scr1.2<- lmer(log1p(BIO_CDA.PhasicMax+.1) ~  TASc*arousal_c * valence_c+
                                           (1 | ssid)+
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract, Group == "NT", !is.na(Alexithymia)))

unique(db_full4new_stim_screen_pupil_nopract$ssid)
summary(scr_models$scr1.2)


db_full4new_stim_screen_pupil_nopract %>%
  group_by(ssid,Group)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  subset(Group == "NT")%>%
  ggplot(aes(TAS))+
  geom_histogram()

interactions::interact_plot(scr_models$scr1, pred = arousal_c, modx = valence_c, mod2 = TASc)



scr_models$scr1.1<- lmer(CDA.PhasicMax ~  arousal_c*valence_c * TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))


summary(scr_models$scr1.1)

interactions::interact_plot(scr_models$scr1.1, pred = arousal_c, modx = valence_c, mod2 = TASc)




scr_models$scr2<- lmer(BIO_CDA.ISCR_collapsed ~  arousal_c*valence_c *TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))


summary(scr_models$scr2)


interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c)


scr_models$scr2<- lmer(BIO_CDA.SCR_collapsed ~  arousal_c* valence_c *TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract, Group = "NT"))


summary(scr_models$scr2)


interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c)

# the effect of pupil is the opposite on scr



scr_models$scr3<- lmer(BIO_CDA.ISCR_collapsed ~  arousal_c * valence_c * TASc+
                                           (1| ssid) +
                                           (1 | stimIAPS),
                        REML = FALSE,
                        data =  subset(db_full4new_stim_screen_pupil_nopract_nt, scr_utl == "notoutlier"))


summary(scr_models$scr3)


interactions::interact_plot(scr_models$scr2, pred = arousal_c, modx = valence_c, mod2 = TASc)

# the effect of pupil is the opposite on scr


```


check scr on within individuals

```{r}

lm_objtest<- lm(BIO_CDA.PhasicMax_z ~ arousal * valence, data = db_full4new_stim_screen_pupil_nopract_nt)
lm_objtest_summ<-summary(lm_objtest)
lm_objtest_summ$coefficients
db_full4new_stim_screen_pupil_nopract_nt

lm_objtest[["coefficients"]][[2]]
lm_objtest[["coefficients"]][["arousal"]]
lm_objtest[["coefficients"]][["valence"]]

regression_df <- data.frame(ssid= rep(NA, 50), Estimate_arousal= rep(NA, 50), 
                         Estimate_valence= rep(NA, 50),
                         Estimate_int= rep(NA, 50),
                         p_arousal= rep(NA, 50), 
                         p_valence= rep(NA, 50),
                         p_int= rep(NA, 50))



lm_ind_summary$coefficients[[14]]

ssid<- unique(db_full4new_stim_screen_pupil_nopract_nt$ssid)


db_full4new_stim_screen_pupil_nopract_nt<- db_full4new_stim_screen_pupil_nopract_nt%>%
  group_by(ssid)%>%
  mutate(arousal_z_ssid = scale(arousal - mean(arousal, na.rm = TRUE))[,1],
         valence_z_ssid = scale(valence - mean(valence, na.rm = TRUE))[,1])

i = 1

rm(b)

# debug
ssid_tes<- unique(db_full4new_stim_screen_pupil_nopract_nt$ssid_num)

table(is.na(db_full4new_stim_screen_pupil_nopract_nt$BIO_CDA.PhasicMax_z))
lm_ind  <- lm(BIO_CDA.PhasicMax_z ~ arousal_z_ssid *valence_z_ssid,
                      data = subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) &
                                    ssid_num == ssid_tes[10]))


subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) &
                                    ssid_num == ssid_tes[10])



```


```{r}
i = 1
rm(b)

for (b in 5:length(ssid_tes)) {
  # for (s in 1:length(nsim)) {

    message(sprintf("$$$$$RUNING lmer for ssid %i", ssid_tes[b]))
    # db_of7_new_JEFFE$VideoType_contrast_sample<- sample((rep(c(-.5, .5), each =  1920)), replace = FALSE) 
        #run the model on the current time bin and simulation sumber
        lm_ind  <- lm(BIO_CDA.PhasicMax_z ~ arousal_z_ssid *valence_z_ssid,
                      data = subset(db_full4new_stim_screen_pupil_nopract_nt, !is.na(BIO_CDA.PhasicMax_z) & 
                                    mediansplit_ground_valence == "More negative"&
                                     ssid_num == ssid_tes[b]))

        #store results from the simulation
            lm_ind_summary<- summary(lm_ind)
            regression_df[i,1]<- ssid_tes[b] #save ssid
            regression_df[i,2]<- lm_ind_summary$coefficients[[2]] # arousal beta
            regression_df[i,3]<-lm_ind_summary$coefficients[[3]] # valence beta
            regression_df[i,4]<-lm_ind_summary$coefficients[[4]] # inyteraction beta
            regression_df[i,4]<-lm_ind_summary$coefficients[[4]] 
            regression_df[i,5]<- lm_ind_summary$coefficients[[14]]
            regression_df[i,6]<- lm_ind_summary$coefficients[[15]]
            regression_df[i,7]<- lm_ind_summary$coefficients[[16]]
            
            lm_ind_summary$coefficients[[14]]
            # simulated_clusters_JEFFE[i,6]<- nsim[s] #store simulation ount
            # timecourse_result_df_neg[i,5] <-ifelse(length(lmer_bin_pup_summary$optinfo$conv$lme4$message)
            #                                        != 0,
            #                                    lmer_bin_pup_summary$optinfo$conv$lme4$message, 'pass')

     i = i+1
}


View(regression_df)

 lm_ind_summary$coefficients[[4]]
# whether I use ground valence, self valemce or this sample average valence, I get the same patterns

timecourse_result_df_neg%>%
  ggplot(aes(timebins, t))+
  geom_line(size = 2)+
  geom_line(aes(y = p), linetype = "dashed", size = 2)+
  geom_hline(yintercept = .1, color = 'red', size = 1.5)


t.test(regression_df$Estimate_arousal)

ssid# just positive
regression_df <- data.frame(ssid= rep(NA, 50), Estimate_arousal= rep(NA, 50), 
                         Estimate_valence= rep(NA, 50),
                         Estimate_int= rep(NA, 50),
                         p_arousal= rep(NA, 50), 
                         p_valence= rep(NA, 50),
                         p_int= rep(NA, 50))


```









```{r}

scatter3D(x, y, z, clab = c("Sepal", "Width (cm)"))

db_full4new_stim_screen_pupil_nopract_nt

install.packages("plot3D")
install.packages("misc3d")
library(misc)

plot3D::scatter3D(db_full4new_stim_screen_pupil_nopract_nt$pup_basCor, 
          db_full4new_stim_screen_pupil_nopract_nt$arousal_c, 
          db_full4new_stim_screen_pupil_nopract_nt$valence_c)

 install.packages("plot3D", dependencies = TRUE)
 
  install.packages("misc3d", repo = 'https://mac.R-project.org')
 
 
install.packages(c("rgl", "car"))

library(car)

install.packages("car", dependencies = TRUE)


car::scatter3d(db_full4new_stim_screen_pupil_nopract_nt$arousal_c, 
          db_full4new_stim_screen_pupil_nopract_nt$pup_basCor, 
          db_full4new_stim_screen_pupil_nopract_nt$valence_c,
          fit = "smooth"
          # point = FALSE,
          
          # surface = FALSE,
          # point.col = ""
          )


```



gg = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(BIO_CDA.PhasicMax_z, arousal_c)) +
  stat_density_2d(aes(fill = stat(db_full4new_stim_screen_pupil_nopract_nt$valence_c)), 
                  geom = "polygon",
                  n = 100,bins = 10,contour = TRUE) +
  # facet_wrap(clarity~.) +
  scale_fill_viridis_c(option = "A")

rayshader::plot_gg(gg,multicore=TRUE,width=5,height=5,scale=250)

scr<- db_full4new_stim_screen_pupil_nopract_nt$BIO_CDA.PhasicMax_z
arousal<- db_full4new_stim_screen_pupil_nopract_nt$arousal_c

valence = db_full4new_stim_screen_pupil_nopract_nt$valence_c




gg = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(BIO_CDA.PhasicMax_z, arousal_c)) +
  stat_density_2d(aes(fill = stat(valence_c)), 
                  geom = "polygon",
                  # n = 100,bins = 10,
                  contour = TRUE) +
  # facet_wrap(clarity~.) +
  scale_fill_viridis_c(option = "A")
rayshader::plot_gg(gg,multicore=TRUE,width=5,height=5,scale=250)
gg
#No lines
pp_nolines = ggplot(db_full4new_stim_screen_pupil_nopract_nt, aes(x=BIO_CDA.PhasicMax_z, y=arousal_c)) +
  geom_point(aes(color = valence_c))
  scale_fill_viridis_c(a)

rayshader::plot_gg(pp_nolines, width = 4, height = 4, scale = 300, multicore = TRUE)
pp_nolines


?scatter3d
scatter3d(x = arousal, 
          y = valence, z = scr, 
          fit = "smooth",
          
          # surface = FALSE,
          point.col = "blue", 
          # point
          groups = as.factor(db_full4new_stim_screen_pupil_nopract_nt$mediansplit_sample_valence2), 
          ellipsoid = FALSE)



scatter3d(x = db_full4new_stim_screen_pupil_nopract_nt$pup_basCor, 
          y = db_full4new_stim_screen_pupil_nopract_nt$BIO_CDA.PhasicMax_z, z = db_full4new_stim_screen_pupil_nopract_nt, 
          fit = "smooth",
          
          # surface = FALSE,
          point.col = "blue", 
          # point
          groups = as.factor(db_full4new_stim_screen_pupil_nopract_nt$mediansplit_ground_valence), 
          ellipsoid = FALSE)


library(viridisLite)
install.packages("viridis")
library(viridis)
deathgg = ggplot(db_full4new_stim_screen_pupil_nopract_nt) +
  geom_tile(aes(x=arousal_c,y=pup_basCor,fill=valence_c))+
  # scale_x_continuous("Year",expand=c(0,0),breaks=seq(1900,2010,10)) +
  # scale_y_continuous("Age",expand=c(0,0),breaks=seq(0,100,10),limits=c(0,100)) +
  scale_fill_viridis()
  # ggtitle("Death Probability vs Age and Year for the USA") +
  # labs(caption = "Data Source: US Dept. of Social Security")
  
rayshader:: plot_gg(deathgg, multicore=TRUE)
height=5,width=6,scale=500

```

heart rate


```{r}





unique(db_full6new$Bio_Mean_HR)
unique(db_full6new$Bio_Mean_HR_fix)


View(db_full6new)

db_full4new_stim_screen_pupil_nopract
  db_full4new_stim_screen_pupil_nopract %>%
  # subset(ssid == 314)%>%
    group_by(ssid)%>%
    mutate(BIO_CDA.SCR_z = scale(log1p(BIO_CDA.PhasicMax +.1))[,1])%>%
    mutate(arousal_z = scale(arousal +.1))%>%
  ggplot(aes(arousal_z,BIO_CDA.SCR_z, color =mediansplit_ground_valence , shape =mediansplit_ground_arousal))+
    geom_point()+
    geom_smooth(method = 'lm', se = F)+
    facet_wrap(~ssid)+
    ggpubr::stat_cor()


```


```

```{r}

# keep stim and and fixation

unique(db_full6new$screencontent)
unique(db_full6new$screencontent_no)
db_full6new_hr_fix_stim<- subset(db_full6new, screencontent_no <3)
View(db_full6new_hr_fix_stim)
# fixation"    "stim" 

# keep controls
unique(db_full6new_hr_fix_stim$ssid)
db_full6new_hr_fix_stim$ssid_num <- as.numeric(as.character(db_full6new_hr_fix_stim$ssid))



db_full6new_hr_fix_stim_nt<- subset(db_full6new_hr_fix_stim, Group == "NT")
unique(db_full6new_hr_fix_stim_nt$ssid_num)

db_full6new_hr_fix_stim_nt<- subset(db_full6new_hr_fix_stim, ssid_num> 303)
unique(db_full6new_hr_fix_stim_nt$ssid)

# HR differences
db_full6new_hr_fix_stim_nt$Bio_Mean_HR_fix<- if_else(db_full6new_hr_fix_stim_nt$screencontent_no ==1, db_full6new_hr_fix_stim_nt$Bio_Mean_HR, NULL)

View(db_full6new_hr_fix_stim_nt)

library(dplyr)
library(tidyverse)
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt %>%
  group_by(ssid, tNo) %>%
  arrange(ssid, tNo,screencontent_no)%>%
  fill(Bio_Mean_HR_fix, .direction = "down")%>%
  subset(screencontent_no == 2)

View(db_full6new_hr_fix_stim_nt1)

db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1 %>%
  mutate(Bio_Mean_HR_dif = Bio_Mean_HR - Bio_Mean_HR_fix)
# 2 1
stim - fix
db_full6new_hr_fix_stim_nt1%>%
  ggplot(aes(Bio_Mean_HR_dif))+
  geom_histogram()

?scatter3d 


db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(pup_basCor))%>%
   subset(!is.na(BIO_CDA.PhasicMax_z))%>%
  subset(!is.na(Bio_Mean_HR_dif))


db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
  group_by(ssid)%>%
  mutate(BIO_CDA.PhasicMax_z= scale(BIO_CDA.PhasicMax, center = TRUE, scale = TRUE)[,1])

scatter3d(x = db_full6new_hr_fix_stim_nt1$pup_basCor, 
          y = db_full6new_hr_fix_stim_nt1$BIO_CDA.PhasicMax_z, 
          z = db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif, 
          fit = "smooth",
          
          surface = FALSE,
          point.col = "blue",
          # point
          groups = as.factor(db_full6new_hr_fix_stim_nt1$mediansplit_ground_arousal),
          ellipsoid = TRUE)



cor(db_full6new_hr_fix_stim_nt1[,c("pup_basCor", "BIO_CDA.PhasicMax_z","Bio_Mean_HR_dif")], use = "complete")



db_full6new_hr_fix_stim_nt1%>%
  group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(pup_basCor,BIO_CDA.PhasicMax_z, color= mediansplit_ground_valence))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ylim(0,.4)+
  ggpubr::stat_cor()



db_full6new_hr_fix_stim_nt1%>%
  # group_by(ssid)%>%
  # mutate(Bio_Mean_HR_dif_z = scale(Bio_Mean_HR_dif))
  group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(pup_basCor,Bio_Mean_HR_dif_z, color= mediansplit_ground_valence))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  # ylim(-7,2)+
  ggpubr::stat_cor()



db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  # group_by(ssid)%>%
  # mutate(Bio_Mean_HR_dif_z = scale(Bio_Mean_HR_dif))
  group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(BIO_CDA.PhasicMax_z,Bio_Mean_HR_dif, color= mediansplit_ground_valence))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  # ylim(-7,2)+
  ggpubr::stat_cor()+
  facet_grid(~mediansplit_ground_valence)
  

```


```{r}


db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif_outl<- if_else(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif)>
                                                             (median(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif),na.rm = TRUE) +                                                                                                        (4*(sd(abs(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif), na.rm = TRUE)))), "outlier", "not outlier")


db_full6new_hr_fix_stim_nt1%>%
  ggplot(aes(Bio_Mean_HR_dif))+
  geom_histogram()+
  facet_grid(~Bio_Mean_HR_dif_outl)

```


```{r}
db_full6new_hr_fix_stim_nt1 %>%
    subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  subset(ssid_num<500)%>%
  group_by(ssid)%>%
  mutate(Bio_Mean_HR_dif_ssid = mean(Bio_Mean_HR_dif, na.rm = TRUE))%>%
  group_by(ssid)%>%
  # mutate(Bio_Mean_HR_dif_stim = mean(Bio_Mean_HR_dif, na.rm = TRUE))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(TASc, Bio_Mean_HR_dif))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  
  

db_full6new_hr_fix_stim_nt1 %>%
    subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  subset(ssid_num<500)%>%
  # group_by(ssid)%>%
  mutate(Bio_Mean_HR_dif = abs(Bio_Mean_HR_dif))%>%
  group_by(ssid, stimIAPS)%>%
  mutate(Bio_Mean_HR_dif_stim = abs(Bio_Mean_HR_dif))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(TASc, Bio_Mean_HR_dif))+
  # geom_point()
  
  geom_smooth(aes(group = stimIAPS, y =Bio_Mean_HR_dif_stim), method=lm, se=FALSE, colour = "gray40", alpha = .01,linetype="dashed", size = .2 )+
  geom_smooth( method=lm, se=FALSE, linetype="dashed",
             color="darkred", size = 2)+
  stat_summary(geom = 'pointrange', pch = 1)+
  
  
  
  # geom_point()+
  # stat_summary(aes(group =ssid, y = Bio_Mean_HR_dif),method = "line",alpha = .1)+
  # ggpubr::stat_cor()+
  p$graphstyle+
  xlab("Alexithymia (Z)")+
  ylab("HR difference")+
p$graphstyle



hr_bio <- lmer(Bio_Mean_HR_dif ~ TASc *arousal_c + (1|ssid) + (1| stimIAPS),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt1, Bio_Mean_HR_dif_outl == "not outlier"))


interactions::interact_plot(hr_bio, pred = arousal_c, modx = TASc)


subset(db_full6new_hr_fix_stim_nt1, Bio_Mean_HR_dif_outl == "not outlier")


```

```{r}
db_full6new_hr_fix_stim_nt1 %>%
  subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  group_by(ssid)%>%
  mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(TAS, cor_hrdif_ar))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  p$graphstyle



db_full6new_hr_fix_stim_nt1 %>%
  subset(TAS<70)%>%
  subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  group_by(ssid,mediansplit_ground_valence)%>%
  mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, cor_hrdif_ar))+
  geom_jitter(alpha = .1, width = .1)+
  stat_summary(geom = 'pointrange')
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  p$graphstyle
  
```




db_full6new_hr_fix_stim_nt1 %>%
  # subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  group_by(ssid)%>%
  mutate(cor_hrdif_ar = cor(Bio_Mean_HR_dif, arousal_c))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(TAS, cor_hrdif_ar))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()

```{r}
db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
  group_by(stimIAPS)%>%
  mutate(ValenceMeanThisSample = mean(valence, na.rm = TRUE), 
         ArousalMeanThisSample = mean(arousal, na.rm = TRUE))


db_full6new_hr_fix_stim_nt1<- db_full6new_hr_fix_stim_nt1%>%
  ungroup()%>%
  mutate(mediansplit_ground_valence = if_else(ValenceMean >=
                                                  median(ValenceMean,
                                                         na.rm = TRUE), "More positive", "More negative"))%>%
  mutate(mediansplit_ground_arousal = if_else(ArousalMean >=
                                                  median(ArousalMean,
                                                         na.rm = TRUE), "High", "Low"))%>%
    mutate(mediansplit_sample_valence2 = if_else(ValenceMeanThisSample >=
                                                  median(ValenceMeanThisSample,
                                                         na.rm = TRUE), "More positive", "More negative"))%>%
  mutate(mediansplit_sample_arousal2 = if_else(ArousalMeanThisSample >=
                                                  median(ArousalMeanThisSample,
                                                         na.rm = TRUE), "High", "Low"))


db_full6new_hr_fix_stim_nt1%>%
  subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  group_by(ssid,mediansplit_ground_arousal, mediansplit_ground_valence,)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif))+
  geom_jitter(alpha = .2, width = .2)+
   geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
  # stat_smooth(aes(group = ssid), se = F, method = 'lm', alpha = .2)+
    facet_grid(~mediansplit_ground_valence)
  
```

```{r}  
db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(Bio_Mean_HR_dif_outl== "not outlier")%>%
  group_by(ssid, mediansplit_ground_arousal,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif, fill = mediansplit_ground_valence))+
  geom_jitter(alpha = .2, width = .2)+
   geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
  stat_summary(aes(group = ssid), geom = 'line', alpha = .2)+
  
 
  facet_grid(~mediansplit_ground_valence)+
     ggpubr::stat_compare_means()
  # p$graphstyle+
  scale_fill_brewer(palette = "Dark2")+


```
  # stat_smooth(aes(group = ssid), se = F, method = 'lm', alpha = .2)


db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(Bio_Mean_HR_dif_outl!= "outlier")%>%
  group_by(ssid, mediansplit_ground_arousal,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_arousal, Bio_Mean_HR_dif))+
  geom_jitter(alpha = .2, width = .2)+
   geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
  ggpubr::stat_compare_means()+
  facet_grid(~mediansplit_ground_valence)


# self 

db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(Bio_Mean_HR_dif_outl!= "outlier")%>%
  group_by(ssid, mediansplit_sample_arousal2,mediansplit_sample_valence2)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_sample_arousal2, Bio_Mean_HR_dif))+
  geom_jitter(alpha = .2, width = .2)+
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1, size = .4, colour = "Black") +
   # geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
  ggpubr::stat_compare_means()+
  facet_grid(~mediansplit_sample_valence2)



lmer(
  db_full6new_hr_fix_stim_nt1)



db_full6new_hr_fix_stim_nt1%>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal))+
  geom_histogram()

```{r}
# test - analyse just positive
hr_dif_models$arousal_val <- lmer(Bio_Mean_HR_dif ~  valence_c *arousal_c+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" & Bio_Mean_HR_dif_outl == "not outlier"  & Group == "NT"))


summary(hr_dif_models$arousal_val)


unique(db_full6new_hr_fix_stim_nt1$ssid)
hr_dif_models$valfrom_hr <- lmer(valence_c ~   Bio_Mean_HR_dif* arousal_c+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" &
                   Bio_Mean_HR_dif_outl == "not outlier" & Group == "NT"))
arousal_c


summary(hr_dif_models$valfrom_hr)

interactions::interact_plot(hr_dif_models$valfrom_hr, pred = Bio_Mean_HR_dif, modx = arousal_c)

plot(hr_dif_models$arousal_val)

car::vif(hr_dif_models$arousal_val)
 # valence_c           arousal_c valence_c:arousal_c 
           # 1.767605            1.708642            1.070635

?step
?lmerTest::step()
lmerTest::step(hr_dif_models$arousal_val)

# favours valence

# check the response bias thing
db_full6new_hr_fix_stim_nt1 %>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  ggplot(aes(log10(arousal+10.5) ,Bio_Mean_HR_dif))+
    # geom_point(alpha = .1)+
    facet_grid(~mediansplit_ground_valence)+
  geom_smooth(method = 'lm', se = F)+
  ggside::geom_ysidedensity(aes(x=stat(density, fill = mediansplit_self_valence), alpha = .4))+
ggside::geom_xsidedensity(aes(y=stat(density, fill = mediansplit_self_valence), alpha = .4))



scatter3d(x = db_full6new_hr_fix_stim_nt1$valence_c, 
          y =db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif, z =db_full6new_hr_fix_stim_nt1$arousal_c,
          # fit = "smooth",
          
          # surface = FALSE,
          point.col = "blue", 
          # point
          # ellipsoid = db_full6new_hr_fix_stim_nt1$mediansplit_sample_valence2)
          ellipsoid = TRUE)


db_full6new_hr_fix_stim_nt1%>%
  group_by(stimIAPS, mediansplit_ground_valence, mediansplit_ground_arousal)%>%
  ggplot(aes(mediansplit_ground_valence, ValenceMean))+
  geom_point()




db_full6new_hr_fix_stim_nt1%>%
  subset(Condition == "IAPS")%>%
  group_by(stimIAPS, mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, arousal))+
  stat_summary(geom = 'pointrange')+
  ggpubr::stat_compare_means()+
  geom_text(aes(label = stimIAPS))

```





check corelation between arousal and valence on and individual basis


```{r}

db_full6new_hr_fix_stim_nt1$arousal_c
subset(db_full6new_hr_fix_stim_nt1, mediansplit_ground_valence == "More positive" & Bio_Mean_HR_dif_outl == "not outlier") %>%
  group_by(ssid)%>%
  mutate(cor_vale_ar = cor(arousal, valence, use  = "complete"))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(cor_vale_ar))+
    geom_histogram()+
  geom_vline(xintercept = -.7)



subset(db_full6new_hr_fix_stim_nt1)%>%
  # group_by(mediansplit_ground_valence, stimIAPS)%>%
  # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes( valence, arousal))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  # geom_smooth(aes(group = ssid), se  = F, method = 'lm')+
  # facet_grid(~mediansplit_ground_valence)+
  ggpubr::stat_cor()



subset(db_full6new_hr_fix_stim_nt1)%>%
  subset(ssid == 331)%>%
  # group_by(mediansplit_ground_valence, stimIAPS)%>%
  # summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes( as.numeric(tNo), arousal))+
  geom_point()+
  stat_summary(geom = "line")+
  geom_point(aes(y = valence), color = "green")+
  stat_summary(aes(y = valence),geom = "line", linetype = "dashed", color = "green")
  # geom_smooth(method = 'lm', se = F)+
  # geom_smooth(aes(group = ssid), se  = F, method = 'lm')+
  # facet_grid(~mediansplit_ground_valence)+
  # ggpubr::stat_cor()
  





```



# try and regress valence our oarousal_val

db_full6new_hr_fix_stim_nt2

db_full6new_hr_fix_stim_nt3<- subset(db_full6new_hr_fix_stim_nt2, !is.na(valence))

hr_dif_models$arosal_from_val <- lmer(arousal_c~  valence_c +
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt3))


hr_dif_models$val_from_arousal <- lmer(valence_c~arousal_c+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt3))


hr_dif_models$arosal_from_val
plot(hr_dif_models$arosal_from_val)

?residuals
?resid
?residuals.merMod
db_full6new_hr_fix_stim_nt3$arous_val_resid <- residuals(hr_dif_models$arosal_from_val)

db_full6new_hr_fix_stim_nt3$val_from_arous_resid<- residuals(hr_dif_models$val_from_arousal)

# residuals.merMod(hr_dif_models$arosal_from_val)

ar_frm_val<- lm(arousal_c ~ valence_c, data = db_full6new_hr_fix_stim_nt3)

val_frm_val<- lm( valence_c~arousal_c, data = db_full6new_hr_fix_stim_nt3)

db_full6new_hr_fix_stim_nt3%>%ggplot(aes(val_from_arous_resid, arous_val_resid))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()


```


resid(ar_frm_val)
resid(val_frm_val)
 #           1            2            3            4            5            6            7            8 
 # 0.149962843  0.505815507
 # 
 # -0.637512958  0.044580602 
 
 
 
Is the residual of A~ B the same as B~A 
```{r}

hr_dif_models$val_from_arousal <- lmer(valence_c~arousal_c+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt3))


hr_dif_models$biohr_dif_arous_res <- lmer(Bio_Mean_HR_dif~  val_from_arous_resid *arous_val_resid+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = subset(db_full6new_hr_fix_stim_nt3, Bio_Mean_HR_dif_outl == "not outlier"))


summary(hr_dif_models$biohr_dif_arous_res)

# analyses of residuals also suggets that effects on bio_hr are related to common arousal and valence relation

```

interactions::interact_plot(hr_dif_models$arousal_val, pred= arousal_c, modx = valence_c)


# alexithjymia
db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif_z<- scale(db_full6new_hr_fix_stim_nt1$Bio_Mean_HR_dif, center = TRUE, scale = TRUE)[,1]
hr_dif_models$arousal_val_cat <- lmer(Bio_Mean_HR_dif_z ~TASc*arousal_c+
                                        + (1 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = db_full6new_hr_fix_stim_nt1)

interactions::interact_plot(hr_dif_models$arousal_val_cat, pred= arousal_c, modx = TASc)

summary(hr_dif_models$arousal_val_cat)

plot(hr_dif_models$arousal_val)
plot(hr_dif_models$arousal_val_cat)




```
summary(hr_dif_models$arousal_val_cat)


hr_dif_models$arousal_val_cat <- lmer(Bio_Mean_HR_dif~  mediansplit_sample_arousal2* mediansplit_sample_valence2+
                                        + (1+mediansplit_sample_valence2 | ssid) + (1|stimIAPS),
                                        # (1|mediansplit_sample_arousal2),
     REML = FALSE,
     data = db_full6new_hr_fix_stim_nt1)


summary(hr_dif_models$arousal_val_cat)

interactions::cat_plot(hr_dif_models$arousal_val_cat, pred = mediansplit_sample_arousal2, modx = mediansplit_sample_valence2)


```
# correlation

```{r}
db_full6new_hr_fix_stim_nt1%>%
  subset(!is.na(mediansplit_ground_valence))%>%
  subset(Bio_Mean_HR_dif_outl!= "outlier")%>%
  group_by(ssid,mediansplit_ground_valence, mediansplit_ground_arousal)%>%
  mutate(cor_hr_arous = cor(Bio_Mean_HR_dif, arousal, use = "complete"))%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(mediansplit_ground_valence, cor_hr_arous))+
  # geom_jitter(alpha = .2, width = .2)+
  geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
  # geom_smooth(aes(group = ssid), color = 'gray', method = 'lm', se = F)+
  ggpubr::stat_compare_means()+
  facet_grid(~mediansplit_ground_arousal)



```
  # xlim(-5,3)

```{r}
db_full6new_hr_fix_stim_nt1 %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(Age, TASc))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()

hr_dif_models<- list()

hr_dif_models$arousal_val <- lmer(Bio_Mean_HR_dif~  (arousal_c*valence_c )* TASc +(1+arousal_c | ssid) + (1|stimIAPS),
     REML = FALSE,
     data = db_full6new_hr_fix_stim_nt1)

summary(hr_dif_models$arousal_val)

interactions::interact_plot(hr_dif_models$arousal_val, pred = arousal_c, modx = TASc)


hr_dif_models$arousal_val <- lmer(Bio_Mean_HR~  arousal_c* valence_c +(1+arousal_c| ssid),
                                  # + (1|stimIAPS),
     REML = FALSE,
     data = db_full6new_hr_fix_stim_nt1)

summary(hr_dif_models$arousal_val)

# plot(hr_dif_models$arousal_val)

interactions::interact_plot(hr_dif_models$arousal_val, pred = valence_c, modx = arousal_c)


```


```

import HRV resting
```{r}

Rest_HRV <- read_excel("Rest HRV.xlsx")
View(Rest_HRV)  

Rest_HRV%>%
  ggplot(aes(RMSSD))+
  geom_histogram()

Rest_HRV%>%
  ggplot(aes(RMSSD))+
  geom_histogram()

Rest_HRV%>%
  ggplot(aes(MEANHR))+
  geom_histogram()

BIO_HRVanalysys$ssid<- as.character(BIO_HRVanalysys$ssid)

left_join(BIO_HRVanalysys, Rest_HRV, by = "ssid")%>%
  ggplot(aes(RMSSD, RMMSSD))+
  geom_point()+
  ggpubr::stat_cor()


# library(readxl)


Rest_practice_HRV <- read_excel("Rest + practice HRV.xlsx")
View(Rest_practice_HRV)
Rest_practice_HRV$ssid<- as.character(Rest_practice_HRV$ssid)

left_join(Rest_practice_HRV, BIO_HRVanalysys, by = "ssid")%>%
  ggplot(aes(RMSSD, RMMSSD))+
  geom_point()+
  ggpubr::stat_cor()


left_join(Rest_practice_HRV, Rest_HRV, by = "ssid")%>%
  ggplot(aes(RMSSD.x, RMSSD.y))+
  geom_point()+
  ggpubr::stat_cor()


Rest_practice_HRV%>%
  ggplot(aes(MEANHR))+
  geom_histogram()
  
# import hrv task
library(readxl)
hrvResultsTask <- read_excel("Summary_hrvResultsTask.xls")
View(hrvResultsTask)

```

combine with data

```{r}
Rest_HRV

Rest_HRV$subject<- NULL
nrow(db_full6new_hr_fix_stim_nt1)
# 2160

db_full6new_hr_fix_stim_nt2<- left_join(db_full6new_hr_fix_stim_nt1, Rest_HRV)


# hrv rest vs hrtaskCallbackManager(
left_join(Rest_HRV, hrvResultsTask, by = "ssid") %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(RMSSD.x, RMSSD.y))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  xlim(0,100)+
  ylim(0,100)


Rest_HRV$hrvcondition<- "rest"
hrvResultsTask$hrvcondition<- "task"

left_join(Rest_HRV, hrvResultsTask, by = "ssid") %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(RMSSD.x, RMSSD.y))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()+
  xlim(0,100)+
  ylim(0,100)


colnames(hrvResultsTask)
bind_rows(Rest_HRV, hrvResultsTask)%>%
  subset(RMSSD<100)%>%
  ggplot(aes(hrvcondition, hfpower))+
  geom_jitter(width = .1, alpha = .1)+
  stat_summary(geom = 'pointrange')+
  stat_summary(aes(group = ssid), geom = 'line', alpha = .1)
```


```{r}
left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
  group_by(ssid)%>%
  mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
    group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(NN50,cor ))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  xlim(0,500)+
  ggpubr::stat_cor()
  
  
  
  
  left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
  group_by(ssid)%>%
  mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
    group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(TASc, NN50))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
      # ylim(0,200)+
  ggpubr::stat_cor()
  
  
  
  
  hrvResultsTask%>%
    ggplot(aes(RMSSD))+
    geom_histogram()


  
  
  left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
    subset(!is.na(mediansplit_ground_valence))%>%
  # group_by(ssid)%>%
  # mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
    group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(RMSSD,pup_basCor,color = mediansplit_ground_valence ))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  # xlim(0,100)+
  ggpubr::stat_cor()
  
  
  
    left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
    subset(!is.na(mediansplit_ground_valence))%>%
  # group_by(ssid)%>%
  # mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
    group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(RMSSD,BIO_CDA.PhasicMax_z,color = mediansplit_ground_valence ))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  xlim(0,100)+
  ggpubr::stat_cor()
    
    
      
    left_join(db_full6new_hr_fix_stim_nt1, hrvResultsTask) %>%
    subset(!is.na(mediansplit_ground_valence))%>%
       subset(Bio_Mean_HR_dif_outl == "not outlier")%>%
  # group_by(ssid)%>%
  # mutate(cor = cor(arousal, pup_basCor, use = "complete"))%>%
    group_by(ssid,mediansplit_ground_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(`LF/HF`,BIO_CDA.PhasicMax_z,color = mediansplit_ground_valence ))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  # xlim(0,5000)+
  ggpubr::stat_cor()
```
  


db_full6new_hr_fix_stim_nt2 %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(Bio_Mean_HR_dif, lfpower))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()

db_full6new_hr_fix_stim_nt2 %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(MEANHR, RMSSD))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()


db_full6new_hr_fix_stim_nt2 %>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(MEANHR, Bio_Mean_HR))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()
  


```


```{r}
# work on this
db_full6new_304_up$
colnames(db_full4new_stim_screen_pupil_nopract)
colnames(db_full6new_304_up)
# db_full6new
unique(db_full6new_304_up$screencontent)

View(db_full6new)

# store heart rate baseline to compute difference
db_full6new$Bio_Mean_HR_fix = if_else(db_full6new$screencontent == "fixation", db_full6new$Bio_Mean_HR, NULL)

# compute heart rate difference to fixation screen

db_full6new_304_up$Bio_Mean_HR
View(db_full6new_304_up)

db_full6new_304_up<- db_full6new_304_up%>%
  group_by(ssid)%>%
  mutate(BIO_CDA.PhasicMax_z = scale(BIO_CDA.PhasicMax)[,1])



pupulresidtest<- lmer(pup_basCor ~ BRIGHTNESSc +(1+BRIGHTNESSc|ssid), REML = FALSE,
     data = testresiddf)

summary(pupulresidtest)


plot(pupulresidtest)

resid(pupulresidtest)
tstresid <- resid(pupulresidtest)


testresiddf$puopil_resid <- tstresid

testresiddf<- subset(db_full6new_304_up, !is.na(pup_basCor))


summary(lmer(arousal ~ pupil_resid + (1|ssid), REML = FALSE,
     data = testresiddf))



summary(lmer(arousal ~ puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax + (1|ssid),
              # (0+puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax|ssid), 
             REML = FALSE,
     data = subset(testresiddf, as.numeric(as.character(ssid))<500))) 


subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
  group_by(ssid)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(arousal, BIO_CDA.PhasicMax))+
  geom_point()+
  geom_smooth(method = 'lm', se = F)+
  ggpubr::stat_cor()

subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
  mutate(median_ground_arousal = if_else(ArousalMean> median(ArousalMean, na.rm = TRUE), "High", "Low"))%>%
  mutate(median_ground_valence = if_else(ValenceMean> median(ValenceMean, na.rm = TRUE), "More positive", "More negative"))%>%
  group_by(ssid)%>%
  ungroup()%>%
  mutate(median_self_arousal = if_else(arousal> median(arousal), "High", "Low"))%>%
  mutate(median_self_valence = if_else(valence> median(valence), "Positive", "Negative"))%>%
group_by(ssid,median_self_arousal,median_self_valence)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
  ggplot(aes(median_self_arousal, puopil_resid,))+
  geom_jitter(width = .2, alpha = .2)+
   geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
  stat_summary(geom = 'pointrange')+
    facet_grid(~median_self_valence)
  ggpubr::stat_cor()
  
  
  
  
  subset(testresiddf, as.numeric(as.character(ssid))<500)%>%
  mutate(median_ground_arousal = if_else(ArousalMean> median(ArousalMean, na.rm = TRUE), "High", "Low"))%>%
  mutate(median_ground_valence = if_else(ValenceMean> median(ValenceMean, na.rm = TRUE), "More positive", "More negative"))%>%
  group_by(ssid)%>%
  mutate(median_self_arousal = if_else(arousal> median(arousal), "High", "Low"))%>%
  mutate(median_self_valence = if_else(valence> median(valence), "Positive", "Negative"))%>%
group_by(ssid,median_self_arousal)%>%
  summarise_if(is.numeric, mean, na.rm = TRUE)%>%
    subset(!is.na(median_self_arousal))%>%
  ggplot(aes(median_self_arousal, BIO_CDA.PhasicMax_z))+
  geom_jitter(width = .2, alpha = .2)+
   geom_bar(stat="summary", fun.y = "mean", alpha = .2)+
    # geom_smooth(aes(group = ssid),method = 'lm', se = F)+
    # geom_text(aes(label = ssid))
  stat_summary(geom = 'pointrange')
    facet_grid(~median_self_valence)
  ggpubr::stat_cor()
  
  
  summary(lmer(arousal ~ log(BIO_CDA.PhasicMax+.1) + (1|ssid) + (1+log(BIO_CDA.PhasicMax+.1)|stimIAPS),
              # (0+puopil_resid + Bio_Mean_HR+BIO_CDA.PhasicMax|ssid), 
             REML = FALSE,
     data = subset(testresiddf, as.numeric(as.character(ssid))<500 &BIO_CDA.AmpSum> .1)))






```





